So, hi, welcome everyone to meet the publishers meet the publisher portrait of an independent press. This event was produced in partnership with the San Francisco writers conference. My name is Karen Edwards and I am one of the librarians here at the mechanics Institute of San Francisco. Coming to you from my driveway in San Andro, California across one day just to be here. So, for those of you who are unfamiliar with the mechanics Institute we are a independent membership organization that houses a wonderful library, which is the oldest in fact designed to serve the public in California, not just mechanics. So a cultural event center and the nation's oldest chess club. Right now due to the shelter in place all of our events are virtual, but I encourage you to consider becoming a member with us. It's only $120 a year, and that helps your support helps us with our cultural activities and our library and all the other fun things that we do that benefits the San Francisco Bay Area. Our speakers today include Sir on Kerr who is the founder of tailwinds press, which is an independent press based in the New York area. We also have Shane race Legland who is a member of the mechanics Institute's writing community. He was born in Western Montana and attended the university there. And he is the author of may we one day pick all the shrapnel from our hearts and so he'll talk about that this afternoon. We also have Maria Espinosa, who is the author of five novels, including the one she'll be talking about today called suburban souls. And she has taught creative writing and contemporary literature at New College of California and English as a second language for City College of San Francisco. But right now she lives in Albuquerque. So the way it'll work today is all three speakers will share their knowledge and discuss their recent projects, and then we'll open things up for discussion. We're kind of a larger group that I expected today which is yay. And so why don't you post the questions that you have in the chat space and we'll fold those into the conversation. And then later we'll open things up so we can all chat together with your microphones off. I mean on. All right, take it away. Thanks. And first of all, I do want to thank Karen, our host and the mechanics Institute this looks like an amazing institution. I'm very jealous it doesn't exist in sunny Connecticut I'm being sarcastic it's not sunny in where where I am right now. It looks like an incredibly rich environment community and I'm looking forward to learning more about it. So I'm sure I'm the publisher. I nominally run the show around here tailings is an independent press and we've been around since 2014. We focus primarily on manuscript length, literary fiction. So the model of the press is pretty simple. People send me manuscripts and I choose the most beautiful and brilliant ones that I like the most and I publish five or six of them in partnership with the authors. So before, so the stars of the show here are really a shame race and Maria. But I just wanted to sort of say a few words about sort of why the press exists and sort of what it's about. So the creation myth that I always tell is that in 2012. I was reading the 600 page tome of poems by Anne Sexton. And as you know she has her own creation myth where one aspect of the myth is that she's she she goes through a lot of difficulties in her life. And in a religious authority, in this case a priest told her during one of these phases, God is in your typewriter. And her poems are awesome. I love Anne Sexton and that's a great thing to be told. But when I read this the first time I felt profound discomfort that over the years turned into resentment. I gradually came to believe, and I think the turning point was around 2012 that the authority to see deep value in art has to consist of a process of self initiation by readers. Reading is not a passive activity. To say that reading is a passive activity is like saying that skiing is a passive activity like like you're just rolling down the hill. Reading is an active partnership in a joint creative endeavor that's intensely personal sacred and extremely powerful. And as long as we wait for someone else to tell us who's typewriter the divine is living in literature will be detached from the people who connect to it most deeply. So that's the first aspect of the creation myth. There's a second aspect of the press that I want to emphasize. So this is not on the face of an Asian American press I haven't published any novels with Asian American themes I don't seek them out. I think once I unintentionally published an author who might have been half Asian when I looked at his press photo but in this is not like an overtly Asian press, but I am Asian American. I'm not with a background assumption with of a certain set of proto confusion values, and one of them is best expressed as this in text, everyone is equal. I don't research people before I publish them. I try not to think about the author's identity at all when I look at the manuscripts. I accept a book and we like the authors and I talk and and people ask me like do you want to know more about what I do I'm like no no that would just confuse me. It should be about the books. It shouldn't be about who you are what you're seeing is like whether you have friends or critics whether you have friends or well known whether you have friends like at all. It should be about what you write. And, and this is the myth of what I was taught was the Chinese imperial civil service that I grew up on with, with these like six week long exams and like that determines her whole life and people spend their entire life studying for this like ultra ultra SAT. And if the press is any strong ideology, it would be that ideal of everyone being equal before what is written down. So every book here represents, and, you know, it's not just Shane race and Maria we publish, I published several a year and sort of they're all, all these other authors of which Shane race and Maria or sort of a representative sample. But the books on my website represent not just this absolutely brilliant book on its own, which is, but it's also what's fundamentally a political statement. I can't think of anything more radical than to allow your own vision and your own aesthetics to be seen and heard, and to help create things that you believe to be beautiful and good. It's very much an honor to publish Shane race and to publish Maria and the rest of the authors. And if I'm hoping that you come away with any other message other than that like these books are great. You should order them on Amazon or Barnes and Noble and probably pals. The other point is to, is, is to urge people to ask the same questions that I asked, where are you unprivileged in your life where are you invisible and unheard. And what do we create with that knowledge. That's enough for me, I think, I think we should do sort of what we did on our initial launch. I think the writers Shane race and Maria are going to introduce themselves and their books and then I'm going to ask some questions, and we can do a little q amp a. So, Shane race, why don't you kick it off this time. My name is Shane is legal. That is a pseudonym but you know it's my real, you know middle name, my parents. My parents were weird. So Shane, Shane race I grew up in Montana. And, you know, after, you know, after high school went to University of Montana. I left university in Montana about 2007. I, you know, came down to the Bay Area just, you know, for work. But, you know, just the amount of work that I was getting in the also, you know, people I knew back in Montana who did not, you know, leave. I really kind of started this story of just kind of, you know, unemployment and just lack of employment, but you know, people not viewing themselves in the proper light so maybe one day pick all our shopping from my hearts right here. It's about that kind of, you know, bad cycle with you people get into of just self of loathing and not, you know, making you know it out making it in life as they wanted to take place in the Bay Area time in Missouri, Montana. And, you know, the main character is a man who believes his wife is way too good for him. And he believes that he, you know, in order for her to have a good life, she has to leave them and so he goes, goes through a plan to get his wife to leave it. And, you know, over the course of the novel, he begins to learn to, you know, care for himself but also more importantly learn to accept love from others. That's great. Maria. Maria you're up are you on mute. You're on mute we can't hear you. Taryn can you unmute Maria. Okay. Yeah, here, I wanted to show the cover first. It's about a really anguished woman and an anguished family. And yet, the whole book is not anguish it's very much about life in the 70s in the Bay Area which I understand is now almost a historical period. And it's about the effect that people who suffered trauma in the whole escaping the Holocaust specifically a family of German Jews cast past this on through their children, and how it affects their children. And I think I all I can say is read the book. It's so hard to talk about something I've just written I find myself really blocked about it. As my own life, I grew up in Long Island, despite my Hispanic name. I was a Chilean writer. And since I never felt okay with my given name I began calling myself Maria. Now I also call myself Paula Maria, which given name, given name. And I lived in the Bay Area for a long time from the 60s until quite recently. So I've seen the changes that happen and I've just seen how particularly one disturbed member in a family, particularly the mother especially can affect everyone around her. More especially in the book I talk about the lack I describe how the lack of communication between people create so much suffering. How people really can go somewhat crazy by not feeling seed or heard or is she wrong says feeling invisible, feeling that they're what they think is not heard falls on deaf ears. They perceive it's just not seen by the rest of the world. The kind of isolation this creates. And in the book, the young the daughter one of the daughters tries to overcome this by moving to a commune in the Santa Cruz mountains again a very 70s kind of field. All I can say is read the book by it. I'll get back to you there's there's much more to say about the book and and I think, you know, during during this, sorry, during this sort of segment, I'll be sort of making observations about the book but you know I personally am very bad at like listening to things, like as a kid I would never sit still and the teachers read to us I was one of these kids we were just like reading under the table and the teacher is like reading at us and I was always pissed off all the time. So, so at these readings I've been trying to get people to like not read and I just like surprise them with questions. So, and these are like, kind of unrehearsed questions when I asked the authors so, so it's sort of an uncontrolled situation I thought maybe the audience might appreciate. But like apologies in advance to Shane race Maria, but but that's just, I try to think about what would keep me interested if I run one of these so that's just an explanation of the format. So, so let's come back to Shane race. So, and this is the second time we've sort of done a launch we had a group launch a few weeks back so some of these conversations are conversations that are continuing from things we talked about a little bit. The first time. So, one of the things that come to mind when I think about this book. It's not fiction it's sort of philosophers who focus on the control of the self. So when I reach back in my mind and ask myself like what is this remind me of there's this aspect of classical stoicism you know Marcus Aurelius and also existentialism you know like Camus the stranger. So, one aspect of these different approaches to living is the idea of self mastery regardless of what crap is happening on the outside.  So, but they result in a very similar demeanor being presented to the external world which is that of extreme self sufficiency. And this goes along with my sense that there's a very strong tradition and literature of men who don't talk much. And I think that's an extricably tied to both the existentialists and to stoicism like you see that in the stranger but you also see that in things that you know are sort of not like last of the Mohicans or Steinbeck and like probably the king of that is like Hemingway you know there's to be this is important because silence in sort of Western society seen as both a sword and a shield silence helps you preserve the core of your of your inner self it keeps you in some sense not pure and authentic, but it's also used to withhold resources, and in that case it's a sword and, and then you become like the passive aggressive cliche. So, I guess my question for Shane race that preamble is, you know what what would you take to be the role of silence in this novel because the silent one of the things I love about the book is that silence is everywhere. The book is like words and it's like a conventional narrative, but you can feel the silence kind of seeping into your bones as you're reading the text, even when people are saying things, you can feel that sense of peace and silence. So I just want to sort of hear your views on that. And one portion of the book I talk about, you know, football, and how the main character doesn't really care about football, but he knows he has to talk about it. He has to know about it so he can relate to other men. And, you know, he kind of comes into this realization that, you know, I mean, a lot of these sports are there, just so men have a reason to talk to one another, you know, and can actually begin to make that conversation with people.  I feel that, you know, people have a real need to say really what's in their mind, but they cannot, because they feel that what's going to be coming out of their mouth will be, you know, chaotic.   One of the big things of this book is the lack of communication. You know, the main character doesn't know what to say to his wife. His daughter is disabled, who's deaf, and they do communicate but only through sign language.  So the idea of silence is, you know, people are just, you know, getting more and more just isolated and, you know, you know, push back because they're too afraid of, you know, all the emotions that they have just spilling out all at once. And I will say, just in response to the, you know, very epistodist, and, you know, the classic storism is, I mean, you know, especially like a manly kind of view of the world of it's really easy to write something down in a book and say, these are the laws that you should fall.   That's okay. Yeah, no, that's, that's great. That's great. This is, so this is sort of, no, and I agree with this. One of the things that I really enjoy about the book and this in some sense, it's, it makes me think of Marcus Aurelius in the sense that they're living according to these. There's a background assumption of, of a stoic principle, I think in your book and also in Marcus Aurelius, but when you, and that's seen in, you know, the way your, the way your protagonist acts and the way he thinks about things. I think there is this kind of surrender and resignation and also strength in the character. That's really, really important to, to the book. I think that I think it also conveys how, how difficult the stoic philosophy is, and sort of what, what the costs of it might be. And, and I think this sort of segues into my second question, which is, his silence is an output issue, just like his daughter's deafness is like an output issue.   You know, reinforces the role he's supposed to play. In the sense, he's not the amount, the amount of silence that he's giving right, you know, you know, he obviously wants to talk more he also wants to speak more, but he's just reinforced that he's not getting that, you know, that input and that this is, you know, just normally what he's, you know, to expect from you.  You know, this is what, you know, he's supposed to be but this is what he cannot really, you know, be. That makes any sense. You know, it makes total sense and it makes sense in the best possible way which is like when you when you read it. It's, you feel the truth of that more than like when you articulate it which shows that it's necessary if it were articulable I guess it wouldn't be the book would be redundant. But it's, it's a very enigmatic very short novel.  There's that aspect of it. But I think there's also this like atmospheric sadness to the book that goes that goes beyond that in his self sufficiency and he's, there's something so mysterious and very compelling about the writing. So, with most authors, I can kind of sort of, I can tell what the literary influences are. And with this book. It's, it's hard for me to see an obvious lineage. And, you know, and there's a little bit of Virginia Wolf a little bit of to the lighthouse, which is like a different book but it, it's important in that to the lighthouse also acknowledges human suffering.  And it's kind of like no but you know people kind of move on and the book goes on and like people, you know still like do their thing. And even though it's been traumatic. And so so who do you consider to be your influences in as a as a literary writer.   Again, we're going to see Marquez, Carlos when days. And actually, the very first version of a very first version of this book started out as a short story, and it was a magic realist. And I really just had a huge disillusionment with magic realism not that I don't like that the old writers. I just didn't realize I just realized that this is not one of, you know, I wanted to write. I would say, if any of the magic realists probably are closest I probably put feels, feels virtual in English, who was a, you know, considered the founder of the magic realism he was a Uruguayan writer, not successful in his life at all. And he started kind of, you know, adding some things in but you know a lot of his works were about, you know, kind of people trying to, you know, you know, make things the best they have. What about Roberto Balanio. I haven't read a lot of Roberto, Roberto Balanio. There's not much of a connection. I, there's not a connection jumping to mind but I mentioned it because you drew. I think magical realism is sort of poorly understood in the sense that, like, I don't like your, I mean for anyone who's wondering that the book, like Shane racist book is not magical realism is it's very realist like there's something that happens there that that's particularly magical in like any way. But, but to me magical realism is about resorting to like, like a world of traditional myth in a way that it doesn't it doesn't it doesn't mean that unlikely things happen or that absurd things happen to me it means that you're, you're touching some other part of everyday life that that is sort of separate from the everyday life of like you know doing the laundry and like you know getting the car serviced. But I, you know it's interesting I can I can actually kind of see that in the sense that the the narrator spend so much time on his own sort of thinking about stuff. There's something, there is something sacred about everyday small tasks. You know he drives, he drives like a sewage truck, right and he just sort of does one like sort of monotonous task after another, but you can see the little the thing that's magical about the book is that he sees, you know the details of, you know, the people he sees with the sewage truck or, you know the weather as he's driving around it's it's those little things that that make that that sort of help his life have coherence in it. So that's, that's interesting I hadn't, I hadn't real that's something I hadn't seen but but now that you mentioned I think I can see a little bit of it. Thanks, thank you for and we may come back if there's more Q&A, but I want to move on to Maria. So, just a few things about Maria's book so one of the things that I love about suburban souls is there's this portrait of this like enraged offended like burn this house down feminism that we have in the protagonist Gerta.   It makes me always think of this comedian Phil Hartman on this from the Simpsons who died very tragically in the 90s and one of the things he said about his estranged wife before he died was, I retreat into my cave and she throws rocks to get me out. So there's a lot of that dynamic between Gerta and her husband Saul who's estranged for most of the book. It's complicated by the Holocaust because they're Jews who barely escaped America as children from the Nazis it's complicated by 1970s feminism and sexual liberation versus this really repressive version of domesticity. And it's complicated by infidelity, but to me the true focus of suburban souls is this primal destructive universal drive to be loved and to be visible when the world refuses to love you, or to see you. The first question to Maria, which has two parts is first. Why do you focus on the theme of need and dependency in your work and why do you focus on on those topics and why does it, why does the book need to take place in 1970s San Francisco. Well, let me start with the first one, the second one, the 1970s San Francisco Bay Area was a time of, how can I say, ferment and hope the 60s whole hippie movement had taken place people were experimenting with sexual freedom with drugs with communal living with all kinds of new terrain they were trying to break old barriers, and they were breaking part of their psychics, one of the main characters is a Scottish psychic, a rather skeptical ethics, rather dubious morality, let's say. And the first question you're talking about about the enraged feminism about not being seen not being heard they get that really connects with Shane racist book about the lack of communication which is really a major theme, and how it need and dependency. I think, as a woman I was brought up very much in this need dependency, sort of matrix of being where a woman was nothing without a man. And the man was supposed to be the breadwinner of the supreme authority the woman was supposed to gain power and sort of manipulative ways. And so she's up against this whole framework of need and dependency of the fact that she needs to be dependent that's ingrained in her somehow, and she hates it. And it's for Saul the husband. He's just been. He needs, again, the dependency is very real they both neither feels quite strong enough to stand on their own feet to feet they need somebody, and that person they're trying to lean on is failing them. Neither of them hear each other really neither of them really see each other or get to the other person is. They're trying to get to their own families which were quite disturbed that there's this generic sense of the whole culture of the whole German culture trying to annihilate them before they managed to get out of Germany when they were children. So there's that kind of subliminal sense underneath. Yeah, that's all. That's, there's so much going on and suburban souls. The aspect of this book that I find fascinating is the ambivalence. So I'm going to bring up something, an interesting fact, which is that Maria has a connection to someone that I have a massive fascination with, who herself had very deep ties to both San Francisco and New York, which is a nice man, like, you know, Henry, Henry and June and I've read all the unexpurgated diaries and I'm like a huge fan right like even the ones that nobody reads like the like the six that came afterwards and like they put out one in 2017 or 2018. I'm like a huge fan so I didn't realize this until we were almost going to press because, like I said I tried to focus on the books, as I sort of look at the manuscripts but I was looking around for a photo on Maria's website and I realized that a nice man had actually responded with Maria and read some of her poems in I think the late 60s and offered, you know, this appreciation and praise of her poetry, which is awesome so to realize that a nice man and I have somehow connected across time and space through Maria and you know her books and it means so much to me as a publisher so I want to thank Maria for that. But that that got me thinking a little bit about Anais Nen and sort of what are the ways in which there are correspondences to sort of her work and to Gerda. So, I was actually reading the latest, the latest diary. Like I said, it's called Trapeze. So, Gerda is deeply conflicted by her life as is Anais Nen. Anais Nen is a Cuban who marries and falls in love with American men. She doesn't like American men, so you read the diary, she doesn't like them, she thinks they're really basic. The last love of Anais Nen's life was an American man who lived in the Big Sur area and this actually reinforces all of her complaints.  This guy reads too much Time Magazine. I think this is the equivalent in the 1950s of someone watching like meet the press all the time, like I hate meet the press, so she can't stand American men. And yet she loves American men and there's this similar ambivalence in her diaries about America in general, because during the 50s apparently Anais Nen went through this like slump in her career where like nobody read her, the American mainstream wasn't picking her up, everyone lost so much money on her books. And so the other half of the diary, which is like completely safe for work is like how much American readers suck. It's like these people are troglodytes, they're boring, they don't understand Freud, they don't read, they watch too many movies. And yet the irony is that history like has shown that Anais Nen's readers I think are, you know, it's a very American, it's a very American milieu, it would be like unthinkable to think of Anais Nen as being situated anywhere but in America to me. And I think of her as like being completely American in every way in the sense that she's an immigrant.  So, you know, one question I have for you Maria is that, you know, how do you see Gerta's role as an immigrant, especially someone from the, you know, old world roots and a stranger as being a part of how she interfaces with her environment in America. And do you think visibility is a deeper issue for her than, you know, not just in her, the personal sexual sense but also in some global social sense as well. That's an interesting question. Before I answer it I'd like to digress a bit and say that my original attraction to Anais Nen was that she wrote in the voice of a woman, I grew up in the 50s, where the writers I read most of them even the women seem to be having a gardeness about their true feelings and a kind of male voice which I found artificial, a kind of masculine pseudo strong voice. And so they were imitating Kermis Ernest Hemingway so I was very attracted to Anais Nen because she began writing. She was one of the first people I wrote began who wrote about real female feelings emotions. And Gerta, I suppose is an extension of that in a very, very extended way. So she came from Europe, where ironically I felt that women were more visible and more respected in Europe than they were in the United States. When I first traveled to Europe, and I was in Paris for a long time, couple years, I felt the women were much, much more visible, more heard, more freer in a sense than they were in our country. And yes they had the traditional roles of homemaker, as they did in Germany. So I'm, I'm wondering about that I think she may have felt the loss of identity and of being seen and heard coming to this country, rather than an gaining of it does that make any sense. Absolutely.  And so you're, you're, and I, you know, I know this from personal experience to your, your views of a culture are colored by your own individual experiences with, with the culture. You know, even if that's not completely grounded in fact. So I, I see so much of these themes that, you know, initially came up probably during the 60s and the 70s and feminism but that are still sort of prevalent in different forms people are still asking the same questions now there's, there's simmering resentment at having, you know, like someone, this this guy like wanting her to bake a pie because it's like it tastes better than like a store bought pie just her rage at like someone like making her do that when she should be like writing.  But, you know, the questions are still there and the cool thing about an ISD and the cool thing about GERDA as a character is that they both have no boundaries right so so they say all these things. The diary, you know, gives an ISD and that freedom and being like, you know, mentally unstable gives GERDA the other type of freedom. But they're able to to say what a lot of people in every day non magical realism life or like actually just thinking constantly. And to me that's the freedom of suburban souls is that like gleeful like, I'm going to burn this place down, you know, type type of attitude and, and it's not even like a political sense right like it's you just want to burn the place down because it's just soft. And there's, I think there isn't enough of that transparency and in a lot of the books I read so I found that very refreshing. When I'm thinking of this in places like Mexico, women are perhaps less visible freedom but they're more deeply respected, it's a matriarchy. And also in Europe I feel there, at least there was a much deeper respect for women and I think the United States is a very male masculine kind of country and its sensibility. Yeah, it's not, but the irony and the irony is that it's not enjoyed right like it's full of, I feel like, you know, when I'm thinking back to the election. We actually had talks, the group of writers that were launching this year had, we had talks about whether we should have launches like before or after the election or we should have like one before one after. Does that like, does that mean I was like maybe maybe if, if, maybe if it went one way we would like burn book burn our books or something or I don't know, but, but I can actually see his point now, which is that, you know, having reflected on what happened in in the I don't think it's a political statement to say that there was a lot of rage because people that were not traditionally considered to be disenfranchised felt very disenfranchised for reasons that may have been like completely valid, but are not discussed at all. And this kind of goes back to sort of my views about, you know, the press and you know what's important in running the press, you know, what, what does visibility mean what is being heard mean. I think that the feeling of being heard is is so complicated, and suburban souls sort of exemplifies that I think, may we all pick the shrapnel from maybe one day pick all the shrapnel from our hearts exemplifies that. It takes, it takes so much I mean Virginia will simplifies it as like a room of your own, but to actually get there mentally and emotionally for any gender any race is is so difficult. And maybe this is coherent to me because like, I picked the books you know when I read the manuscripts and, and so this is kind of like the headspace behind my behind like this sort of set of books I put out, but it's I'm not sure exactly where I'm going with it but but it is about visibility of sort of groups that feel profoundly disenfranchised. You know, maybe I'm not not having any like sort of social theory background, I can't say what to make of that but, but it is something that that you see and that I think is becoming more prevalent and literature and you know blogs and things. I see a connection between all may all the shrapnel fall and and suburban souls this great. I kind of will the lack of communication that causes so much suffering. And in Shane racist novel, there's a beautiful on a poetic sort of bleakness to the whole thing. And that hard fact as you talk about being stoic. And then saw is feeling some of that in suburban souls. He's a lot less stoic but again, he's not able to express himself just to speak in a way that connects with people. But if only he could think of something to comfort his daughter Hannah but he does he's not able to all he can do is scold her for forgetting some kind of household chore, and he's suffering inside but he's not able to reach out of himself to reach her. And that's the flip side of stoicism. Yeah. That's really good I mean I, every time I, I talk about these, these books I come up, you know, I, I see something different. Teran is doesn't make sense to move to Q&A is now having tormented Shay Grayson Maria for long enough. I think that maybe has the time. Yeah, I do have a question for you sarong. How, how many submissions do you get. If I publicize that I got like 1000s and if I don't publicize that I got like about 300 per year by worth per round. You know, I, lately I haven't been publicizing it that much because I've noticed this is like very mysterious like, no matter how many submissions I get about each year I find about five or six that I like. And I don't know how that shakes out, but it's, it's difficult I read them all myself right so it's, it's a difficult process. I think the writers have incredible patience I think Shane race, like, I think your manuscript took, like, like a really unconscionable length of time for me to sort of get through it like I requested the manuscript and like that took another six months, you know it's, sometimes life just gets ahead of me. And production sort of takes up a lot of time and then once you get really behind you kind of like hide from it so I think after these books fully launch off to start reading the slash again. It's so discouraging because some of it's like so old, you just got like shame to yourself, but but it is rewarding once you once you find a manuscript that that you really want. And it's great that there's so many people writing and producing great things. Now do you have a, like, what's the process people submit a manuscript to you, you read it when you get to it. And then, how does it work for independent presses. Yeah, it's a it's so, you know, I'm, I try to be as informal as possible in the sense I don't have like a specific date, you know, people, when people want to send me something they send me something. And I don't have specific reading periods. I, I take simultaneous submissions. So, and I think that accounts for, you know, a lot of the volume that I get. It is very the submission and the reviewing the manuscripts aspect of it is actually the most difficult one because the rest of it you can kind of predict how long it takes to produce a book. But the uncontrolled factor here is sort of whether you come up against manuscripts that are that require you to like think about very closely whether to take them. Most manuscripts are either like I love them at first sight, or, and, and like even just skimming them like I know I want to take this, or even a skimming it like I know I would never publish this so those decisions are actually made very very quickly. The things that are difficult or the ones were like, I have, I'm kind of conflicted about it. So it means I have to read it like relatively closely and you know that gets pushed to the back burner and that that can drag the process on for a long time. That's the toughest part, like having to make that that decision. Right, I'm sure. And then I have one more question, you know, on the nuts and bolts side of publishing. When, when you decide that you like a manuscript, do you then advise the author to have an editor go over it with a fine tooth comb and continue like shape it, or do you have, do you have your own team that you call in at this point. So, so, and this is idiosyncratic but I don't buy fixer uppers I've done it like once or twice. And I was like, never again. So if something comes that that I like, I trust the process and you know, that's the way it, I just buy it like as a finished product right like I'm not, I'm not going to re sand the floors and like build a patio and like re dig the basement or and, and, and I think that's, that's partly because there's a this is a very, very lean small scale operation, but I think partly I, you know, who knows, you know whether that paragraph should be split up into two into two paragraphs right I think reasonable I don't believe in there being one universal in one universal attitude towards craft I think craft comes in many, many different forms. And, and, and if the general impression is one that is aesthetically that is aesthetically positive. I would not tinker with that, because you know that's sort of the prerogative of the author who had the initial sort of verbal vision of what to write. So, I think that's idiosyncratic that I have this sort of non, not non judgmental but I don't have strong views about how specific things always need to be said or presented. You know, I try to take a more intuitive approach. And, you know, maybe I, I think the results are pretty good but but honestly like who knows you know like that that's sort of the spiritual exercise of running the press you have to sort of believe at the end of the day that that you got it right. Right well I think from the author's point of view you just want to know how this, how this publishing situation is going to work with this publisher, because there's so much variability, and you know you just want to know going in. All right, I need to get my, my ducks in the row as far as the manuscript is concerned make sure that you know I'm not creating any, anything awful that I'm actually producing the best product as possible so. Yeah, and I trust and I think writers kind of know, you know, like, I think they're, I think when people put together, like a large work of fiction, people, most, most people who sort of have, who know what they're doing, like, it's a coherent vision, in the sense that it, you know the, like, you can sort of debate, you can sort of debate back and forth like should it be this way or should it be that way. But, and I always feel like I always lean towards trusting the author more than maybe a lot of other people do. And also is that logistically and that this goes sort of to the role of an indie press logistically, I don't have the, the resources to buy to sign a manuscript that needs like a lot of work and a lot of like, you know, TLC like I accept that whatever, whatever whatever manuscripts I take are sort of in their essentially final form. Right. And Christine posted a question that she would like to hear more about Shane Race's title and how that came to pass. Yeah, I talked with a seal wrong about it during the launch event. The influence is we talked to Ben was a Japanese writer by the name of Kendra Burrow away. And he was known to, he's still alive. He's known to write some really, you know, interesting titles whose works like teach you, teach us about war madness or rise up you young men and generation, just really beautiful titles and that was like kind of the point of, you know, having such a, you know, you know, long title that was always interesting to me. The actual title refers to cynicism and negativity and just kind of the, if you allow it that you know that cynicism can really build up over time and you know maybe one time. I learned like over cynicism, you know, we pick all the shrapnel shrapnel being the cynicism from my heart. That's interesting that you mentioned being influenced and paying attention to other people's titles. Because I find that I spend a lot of time looking at titles and you know they convey so much information about the theme and the tone and the feel of the book. So, yeah, that's interesting. Does anyone else have any other questions that you want to pose in the chat space or shall we, shall we open this up to just general conversation. Not, I don't see that anyone else has questions so what I'm going to do is go ahead and open things up for general conversation. So if you would like to chat let's unmute yourself. Hi Linda are you. Yeah hi. No, okay. Hi anyway. I guess I would like to thank both Surong, shameless and Maria for a great presentation. I liked that it did not follow a traditional form. It was very to think of being one of the authors, but I thought that the authors. They have extremely interesting responses, and that it made her an extremely successful question. Thank you. Linda has a question for both the authors, did you have, did you pay for an editor to go over your manuscripts before submitting the first time around to tail? I'll start. I did get a line editor. I didn't do that the full developmental editing just because I knew that, you know, it's a small book.  I forget what it's called. There's a directory of small presses. I'll try to find it. And yes, I also did I got. I hired an editor to do sort of a rough look at the whole thing, not particularly with typos which is roughnesses. It's very much in shape when I gave it to her. And yes, she did do typos too. That's good. I mean, hiring an editor can be a wonderful experience or you could just really plow a lot of money in there because yeah, what the editors opinion of how your book should. What shape that book should take. You really need to have one, at least someone to go over and make sure that you've, you know dotted your eyes and cross your T's, or vice versa. Let's see, Maria what gave you the inspiration or ideas for your story this is from Sandra. You know I'm thinking about it, and I was thinking a lot about my mother which I hadn't thought about previously growing up in the 50s. And how oppressed, she felt by not just her husband but by the whole set of circumstances by the whole milieu, and also very much by my father who was very much engrossed in his own work as he was a sculptor was and a teacher very engrossed in his own life and career. And so insensitive really to what was going on with her. And finally actually she had a lover who I really liked he was one of our great family friend and his wife and I really was not a good I thought of mine but I thought you know she'd be better off with him he's much more sensitive and empathic and really disturbing her and then I also happened to know a lot of Bay Area holocaust survivors people who got out of Germany or got out of Europe just in time and I go few that had to go through the whole camps to and how it how it affected them on a very deep level and how it ties into a whole centuries long since almost ingrained I think in so many Jews certainly I feel it's ingrained in my old genetic code of just being on ready for the next pogrom to break out ready to flee and a whole set a whole chemistry that sort of revolves around that really does encode in the body cells I think. Yeah I wonder what what's encoding in our. In our physical memories you know now with what we're living through interesting. Let's see does anyone else have any other questions. Would you like to chat directly with the speakers. Feel free to turn your mic off I mean turn it on and say hello. Let's let's do that. Hi Christine it's nice to see you haven't seen you in a while. Now it's so good to to be here at least virtually if not in person. Right. This is happening. Anyone else have any comments for our speakers or me or each other. Well, I guess I wanted to thank you for hosting this whole event, and I wanted to thank Shane race for inviting me and zero wrong for inviting me and really appreciate it. Yes, I've ordered your books to be available at the mechanics Institute's library so. And I'll come in because things are a little bit uncertain right now with with males and with book ordering but it will eventually be in our collection and of course our catalogers are only coming in coming in once a week. So, but eventually it will be there and you'll, you'll see it on the shelf. Oh, that's, that's great. Oh, there's a question. I was going to say thank you to Taryn, and you know Shane race and Maria for sort of letting me publish their books and also to Taryn for hosting this event and I mean, this is, this is awesome. I do appreciate it very much. There is another question from mana, she asks. Exactly to Maria if you have any advice on how to deal with people who are Holocaust deniers. Wow. I don't really know. Yeah. I think it's the way I also feel that I have to deal with people who are Trump supporters. What can I say. You know, Holocaust deniers in the same ways they deal with Holocaust, just deny them, just like, you don't exist.  Right, right. Yeah, actually, I can understand some Trump supporters because I understand a lot of the economic problems that people have gone through and the fact that people are disenfranchised. But yeah, I don't know what to say to the person who denies the Holocaust. Just like that a very different worldview. Yeah, that might be what it's all about. All right, well, I, I think we're losing some of our audience so I just want to thank everyone for coming and thank all of our speakers. You surround you are an amazing interviewer so I look forward to working with you in the future. Yeah, this is an amazing really powerful platform. I'm, you know, this, this is amazing. I'm really, really happy that, you know, you're able to host this and came together. That's great. All right, well we'll be in touch. Thank you everyone. And I look forward to seeing you virtually at another one of our programs shortly. Thanks so much and have a great weekend. Thank you. Bye.


At Big Data SV 2014 is brought to you by headline sponsors, WAN Disco. We make Hadoop invincible and aptian, accelerating Big Data 2.0. Okay, welcome back everyone. This is SiliconANGLE and Wikibon's theCUBE, our flagship program. We go out to the events, extract the signal from the noise. I'm John Furrier, the founder of SiliconANGLE. I'm joined by Coast, Dave Vellante, co-founder of wikibon.org. And we're here in Silicon Valley, live in the heart of Big Data, Silicon Valley country, covering all the actions, startups, VCs, new big companies growing, and of course covering the Stratoconference right across the street. And my next guest is Monty's. We've been a CEO of Splice Machine. Welcome to theCUBE. Thank you very much. So you guys, hot startup, SQL on Hadoop, which has been a hot topic now going back two years. You guys just announced, just within the past 48 hours, $15 million of a Series B financing, which is validation. We were talking about that. Congratulations. And how do you feel? I feel great. Company is now on its second stage of financing, growing significantly. And we think we have a wonderful position where as everybody who is focused on SQL on Hadoop, they're so focused on analytics and having a repository for data science, whereas Splice Machine is really looking to power real-time applications with real transactions. And so we feel being the only real-time transactional SQL on Hadoop database, we've got a unique position in the marketplace to take advantage of that new financing. So we've covered, HADAP two years ago got fresh startup of the year. Dave, we've covered them. We remember we were at the Strata here and they won the startup competition. Some say the ballots were stuffed a little bit, but that's always the Twitter following. That's like getting your picture on that cover of Sports Illustrated, isn't it?  Yeah, and they were the rising star. They won hot a startup. But what's your take? How do you guys different from, say, HADAP? And again, we're gonna start to see the bloom come off the roads. And a lot of these startups, you guys are clearly clearing the runway with the Series B.  You've been there before. What do you guys do differently that the market needs and how do you see the market? I think it's a great question because there's so much interest on SQL and Hadoop and of course Hadoop was one of the first organizations to do that and we hear so many people getting into the space. But we're very different from all of the other players because most of these players have developed architectures for analytics only. And what that means is that they can't serve real time applications. They can't change records on the fly. They can't do transactions. And if you're going to be a general purpose database that's going to power applications on the Hadoop infrastructure, you need all of the services that traditional databases have. Databases like MySQL, Postgres, Oracle, SQL Server, these are the general purpose databases that today are hitting the wall. And now people are looking for scale out solutions and they can scale out on NoSQL but of course they give up on all the SQL services or they can scale out on the NoSQL architectures that are out there that are unproven proprietary architectures or they can scale out on Hadoop which is proven technology to work at petabyte and beyond scale and that's really what we're trying to bring people to, to power applications, real time applications and these other architectures are really about doing data science. So we had Kurt Monash on theCUBE last summer, John at the Vertica user conference and I don't argue with Kurt Monash about the database stuff anyway because it's just one of those. I mean, no matter if you're right, you'll still do it. You'll still do it. It's the point, right? You'll still bury me. But I did ask him, you know, what percent, and sort of your discourse just then reminded me, what percent of the sort of new applications really need, now you didn't say asset properties. First question, is that what you're talking about? Is it asset properties? What percent of these new apps really need those asset properties and I wonder if you could comment on that and then I'll share with you what Kurt said. I love that. I spoke to Kurt last week as well and he's a fantastic character and we're having a great dialogue. I think we're gonna be really having an ongoing communication because he really gets this space to your point. But yes, splice machine is fully acid and the question is, what use cases is that good for? Many people think that asset properties are just for traditional, transactional types of operational applications. These are the applications that people think of that may be powering a website or doing financial transactions, e-commerce and things like that but really, really transactions are required even for OLAP. If you wanna update secondary indexes in a SQL database at the same time as the data and keep that consistent, you need a transactional context. That requires asset properties. That requires a traditional architecture that allows you to keep those atomic consistent and isolated transactions in the database and that's precisely what we're seeing out there. Even if you're doing a large scale reporting application or an analytics application, you actually do need transactions. You need asset properties and that's why we built what we built. Yeah, so one of our other CUBE alums, I was going on Twitter with Ray Wang the other day and we've actually forecast the sort of SQL and no SQL spaces and there's a lot of growth in SQL. I think the Hadoop has been a tailwind for SQL. I think so and I think that people still to this day think of Hadoop more as a static data repository, something that you do data science on. They don't think of it as real-time yet, even though HBase has been around for quite some time and powers many different real-time applications, but today I think we'll start to see real-time SQL-based applications powered by systems like Spice Machine. Well, and we get discussions all the time with some of the early Hadoop practitioners like Stefan Groshup from Datamere. So that's not what Hadoop was designed for. What are you talking about, Monty? Well, I just beg to differ.  Well, what gives me confidence is what our customers are doing. We have one customer who's got this tremendous direct marketing service that they're providing for retailers, large-scale retailers out there and they have implemented an off-the-shelf campaign management software package that's powered by Oracle. That happens to be a campaign management application that's sold by IBM and it's powered by Oracle. They hit the wall and they said to us, listen, we really need to scale out to make our business more profitable and to grow the way we want to grow and we're thinking about replacing the Oracle piece of this. Can you do it? And so they gave us a bunch of queries to test that have been giving them problems and we just blew it out of the water. We just were performing better on much less expensive hardware and then we connected it up with their Cognos systems and their Abinicio systems which were essentially used as their ecosystem for data and everything worked fine. And now we're integrated doing full end-to-end campaigns on their campaign management system proving out that Hadoop can power a real-time application. So that's an example for you. Monty, what was the catalyst for that example? We're talking about replacing Oracle. Was it just the sort of Oracle squeezing them and license costs? Was it the business outcome that they were desiring? They knew they couldn't get it with Oracle. I wonder if we could talk about that later. I think that, I don't know the details too much about why they turned to us but I do know a few of those pain points. Number one, they were having some performance problems in the Oracle architecture they were using today and they were looking on expanding. And I suspect Oracle was probably looking for scale up to Exadata or perhaps in other ways scaling out but I know that they were looking at how they would stay on Oracle and still serve their issues and that was quite expensive. So it's a combination of this price performance issue that they're really trying to get at. And I see that across the board in many of the companies we talked to, they hit the wall with their existing database, they're looking to find some solution, they need to scale out and they really didn't know they can scale out on Hadoop yet and with us coming in and educating that Hadoop is real-time, we're seeing some traction. Let's talk a little bit more about a splice machine, the company, where you're at with your funding and your head count and things like that, all the key metrics there. It's a fun day for us because it's a day after our Series B financing, as you mentioned earlier. So we raised $15 million from two great venture capital partners, our original investors, more David Dow ventures as well as InterWest partners. We are excited to take the company to the next stage. We're about 30 people. We're in private beta. So we're a young company still, having a number of about 15 companies testing our system at scale with their use cases, like the one I mentioned earlier. And we'll be in public beta sometime very soon. I look forward to reaching out to you guys to announce that, but we'll put our platform out on our website for anyone to try, download, and even use at small scale. So you'll see that public beta coming real soon. What are some of the success points that you're looking for out of the public beta? Public beta is actually pretty important. We have our startup at our crowd chat, which is actually public preview beta. I want testing at scale. What does that mean scale? And what are some of the economics involved? You don't have to give us the exact numbers, but kind of order of magnitude, how big a scale are you talking about both technically and then on the outcome side from the business model standpoint, what does that render into? I mean, I'll say $15 million financing. You got to show some traction. So just give us a little taste. Sure, sure. So some metrics of success for me. I'd like to go into 2015 with thousands of nodes deployed out there. I'd like to see, I would say, south of $5 million of contracting activity for a first year of a startup company having a generally available product. Anything north of that would be an impossible task, I think, but I am very excited about reaching those objectives. I think that the interest levels that we're getting in conferences like this here at Strata as well as just on our website, without even having our generally available product tells me that this is a market that's quite hot out there. And with respect to the scale of the kinds of problems that people are trying to solve, I would say they're always greater than terabytes, obviously, because otherwise traditional database technology would serve them well. But tens of terabytes and hundreds of terabytes are the sweet spot, I think, of this marketplace. And then there are a few players out there that have petabytes of problems. The tests that are being done by our charter customers, the beta customers I talked about are the kinds of things where 20 billion or 100 billion rows are being joined against a similar size table. So we're talking very significant size that would overwhelm traditional technologies. And it's remarkable at how easy it is for companies to actually generate that kind of data and make use of it today. That's fantastic. And total finance that you guys raised, A and B total, what is that coming through? It was $15 million in this round and $4 million in the first round. I'm not a fan of raising humongous amounts of money. I think that can take startup teams and defocus them if they have too much money. I think you should raise exactly what you need and that's what we need. Great. And obviously the first round, it's not that a lot of cash for that, given the scale you are, but great validation. Again, this is a market of validation. Right now we're seeing this in the big data. Monty, thanks for joining on theCUBE. I'm John Furrier with Dave Vellante. This is big data, SV, we're covering all the action, entrepreneurs, investors, big companies, big moves here in Silicon Valley, covering all the news of the Stratoconference, all the tech athletes. This is theCUBE, right back with our next guest.


Most of this information comes from the Schedule A Tax Year 2022 instructions you could find at the IRS website, irs.gov, irs.gov. Looking at the income tax formula, we're focused on the itemized deductions, remembering first half of the income tax formula is in essence an income statement where we have income minus the equivalent of the expenses being the deductions gets to the equivalent of net income. Not being taxable income, everything's flipped on its head for taxes. We want taxable income, in other words, as low as possible as opposed to normally when we want income as high as possible. In prior sections we talked about what needs to be included in income. We talked about what can be called the above the line deductions as opposed to the below the line deductions or the adjustments to income to get to the subtotal of the adjusted gross income and important subtotal when used to do calculations for phaseouts of say deductions and credits as income levels rise. Now we're on the below the line deductions, greater of the standard or itemized deductions focusing specifically on the itemized deductions which we would only take, we can only get an advantage from if they added up to some amount greater than the standard deduction. Coming up page one of the form 1040 we're down here on line number 12 standard deduction or itemized deduction whichever is larger remembering we would only be attaching the schedule A taking the itemized deduction if the amount was above the standard of 12,950 or 25,900 single verses married respectively. This is the schedule A. Now note when we think about the schedule A the main thing that pushes people over from taking the standard deduction to the itemized deductions is the home ownership because that will typically result in a loan. The loan interest is the big deduction component followed by the property taxes on the home. Once that has been cleared then we can look more in detail about other itemized deductions which on themselves or in and of themselves would not have pushed people over from the standard to itemized deductions but once cleared once we have cleared that itemized deduction threshold then they help they can help a lot more readily such as the gifts to charity. So now we're talking about charitable type of contributions. Now note in prior years what happened is they increased the standard deduction a few years ago and when that happens it actually makes the schedule A a little bit less relevant so some of these favorite deductions these are some of the most popular deductions that are on the schedule A were made a little bit less relevant when they increased the standard deduction which was kind of the point because they were trying to support accounting instruction by clicking the link below giving you a free month membership to all of the content on our website broken out by category further broken out by course each course then organized in a logical reasonable fashion making it much more easy to find what you need then can be done on a YouTube page. We also include added resources such as excel practice problems PDF files and more like QuickBooks backup files when applicable so once again click the link below for a free month membership to our website and all the content on it. And usually the people that are benefiting from the itemization the itemized deductions are more wealthy individuals but you can see how people like their deductions for whatever reason they kind of seep in we can see what will happen in the future in terms of if you make the itemized deductions less relevant then in future law are they going to try to reverse that lowering the standard deduction or reverse it through increasing inflation so the standard deduction is not as relevant in terms of a dollar amount because of inflation decreasing the value of the dollar or you can imagine some of the stuff from the itemized deductions they creep it over into some other area such as with the charitable deductions they put a little they put a capacity to deduct some charitable contributions on the first page of the 1040 now that basically got reversed so that when you think about charitable contributions you're generally thinking the Schedule A which is what we're talking about here and if you're deducting them on the Schedule A you're only going to get a benefit from it if you're basically clearing the standard deduction because otherwise you're not going to get a tax benefit now obviously when giving to charity the only reason to give the charity shouldn't you would think B to get a tax benefit but you can also see why the tax code would try to incentivize you know gifts to charity so this is another one of those instances like many of them on the itemized deduction where it kind of deviates from the standard rule what's the standard rule if you have an income tax system your tax you allow people to deduct the expenses deductions which they needed to consume in order to generate the revenue so you tax people on net income as opposed to gross income which we can clearly see on say for example a Schedule C where you have an income statement income minus the expenses those deductions you needed to expand in order to generate the income gets to the net income so we tax people on the net income not the gross income now when we talk about other types of income like a w2 income then the government would like most people to have w2 income because then they can kind of force the employee error to take the responsibility for a lot of it a lot of the reporting and whatnot and they can we can simplify the tax code by saying we're not going to give you any normal kind of business deductions because we assume your employer is going to take care of that stuff and therefore most of the deductions we kind of think about are these Schedule A deductions and they're strange from an income tax system meaning they're not a natural kind of deduction because they're not necessary in order to generate revenue they're the government trying to incentivize us in somewhere or form trying to change our behavior as clearly as the case with the charitable contribution so you can make the case as to whether or not they should be deductible or not and how much that might benefit society or whatnot but it's clearly again it's kind of outside of the normal classification of a deduction and it's generally on the itemized deductions okay that said we've got the standard deductions note that you have to clear the standard deductions before you'll be able to deduct the charitable contributions so in other words if someone is nowhere near clearing these standard deductions it might not be worth your time to be collecting all of the charitable contributions that someone made or spending all your time during the year trying to try to make sure that you you calculate all the all the gifts that to charity that you make right and because you may not clear I mean if you're only doing that for taxes so but if you are clearing them then it does make sense because you need to have the receipts of who you paid if you're going to be taking the deduction for the charitable contributions so remember that's 12,950 that you would have to clear for single 25,900 for married head of household 19 four and then you've got these deviations and variations if they were over 65 and or blind


Hello everyone welcome to the course of business forecasting. Today we will discuss different components of time series and measures of forecast accuracy. In general in machine learning and predictive analytics models when you analyze different time series data you need to understand the data behavior their pattern and their trend. So, in a time series models there are various type of components exist. For example, it could be trained up trained down trained it could be seasonality also like in India most of the companies sales or say you know products demand follow seasonality. So, seasonality is a one such component. So, what are the components exist in time series data that we are going to discuss at the same time when you make some prediction using any time series models you need to bring the accuracy of your forecast right whether the prediction is right or wrong or good or you know not good. So, that how will measure in comparison with other model or some competitors models or say you know friends model. So, in that case you need to use some measure of forecast accuracy. So, there are many methods of measure of forecast accuracy are there like you know MAD mean absolute deviation RMSC mean absolute percentage deviation. So, all this the mean square error. So, we will study all this measure of forecast accuracy along with components of time series in this session. So, these slides talked about you know different type of models that we are going to cover throughout this course of business forecasting. Apart from that many more aspects will come into different sessions, but this is a overall you know different techniques that we are going to cover whether it could be qualitative methods time series methods and then you know causal models or regression models then simulation models. We have already discussed qualitative methods in the previous session. So, once the that components of time series are completed today and then different measure of accuracy will be covered then we will enter into the different type of time series models. So, let us discuss about components of time series and different type of measure of accuracy what exists in the literature that we need to study to measure which model is better or which model is performing best as compared to the other models. So, that measure of accuracy also we will study today. In general apart from the qualitative I talked about time series causal models and the simulations models all these aspects actually will be covered in this course. Let us focus about little bit of you know recap that what we have covered in the previous sessions that. So, actually in time series when it comes to the time series qualitative aspects I have already discussed. So, we are not going to discuss that today. So, when it comes to the you know time series forecasting the data are first data or say historical data in that case it could be quarterly data, it could be monthly data, it can be say you know daily data, weekly data, it can be yearly data also. Suppose you are taking the like say population study you want to do a population study and you want to make a forecast or say you want to have say you know sales of a company for a yearly basis how their performance are going on. So, in that case you might require yearly data also, but in general in India especially you know over the world itself generally data are being captured quarterly basis or monthly basis. Especially in India if you think about you know the company's arming and their profit growth etcetera or sales all these things are been done quarterly basis. So, we will discuss that when you will go to the seniority and all these analysis we will discuss, but that aspects I am talking to you is that you know data could be the data that you collect could be different type. So, when the data are of different type that means data might have a different nature or different pattern. So, that pattern we need to study how this different pattern of the data can be classified that we are going to discuss. It could be trend analysis, it could be you know to some extent relationship with other parameters also like regression analysis kind of thing causal relationship between independent variable dependent variable that understanding also you need to take into account for your analysis of the and the future forecast. As well as the for the time series data you need to see what kind of past pattern the data has already shown and how it will how it can behave in the future. So, that mapping or interlink need to be captured in a better manner and what is the goal of time series. The goal is to you know understand the data remove the anomalies and clean the data and make a better forecast with the goal or the context of the problem or the industry problem that you have kept in your mind. So, accordingly you have to make a forecast. Let us see the data could be you know cross sectional data it could be time series data. So, when it comes to the cross sectional data what happens the understanding that I am trying to share today. For example, say you know if you take a data at the same time period for different variables the time period is fixed like time that you are measuring that time is suppose in a month of January you are counting right. So, in January or in a particular day you are counting the data say temperature you are counting for different city. So, you are calculating the temperature for different city say Bombay Delhi Calcutta Chennai Bangalore say you are you are counting the temperature for a same at the same time period for different cities and maybe one variable maybe many variable like you know you can see here in this example humidity wind speed. So, there might be many more examples I can show you the where many variables will be there and you are measuring that variables or the data of you are collecting data for them for different from different locations demographic location say at the same at the same time period, but when it comes to the time series data we are focusing only single variable and that for single variable you are collecting the data over a period of time like say January February March April May June this way you are collecting the data or this quarter second quarter third quarter fourth quarter fifth quarter sixth quarter that means next year you have gone. So, this way you are collecting the data for same variable over a period of time here is the example of time series data you can see here here you are collecting the data of say profit or say demand whatever here I have mentioned profit for different year 2001 what was the profit 2002 what was the profit. So, this is called time series data that means for a single variable you are collecting the data and you study the nature of that data how you behave in the past and make a forecast for the future. So, this difference you should understand that which type of data you have whether you have a cross sectional data or more mixed of panel data or you have a time series data. We will be focusing today on time series data and their behavior analysis or competent analysis. One more part that you should know for understanding the data that data in general time series data could be classified into two category it can be classified primary data secondary data different way it can be classified also the primary and secondary part data analysis part also I have discussed little bit of you know in data decision making session, but let us focus on today's session. So, when it comes to the time series data it can be stationary it can be non-stationary what is stationary data stationary data or to some extent stable data that means over a period of time the mean and variance of the data won't change it will remain steady or stationary. So, that data is easier to study the past behavior and easier to make a forecast for the future and the accuracy level which I will show you today the accuracy level also remain quite steady quite better actually forecast accuracy or the you know percentage of reliability will remain quite high, but when it comes to the non-stationary data where that over a period of time the data fluctuate too much. So, when you calculate a block if you take a block of the data and if you measure the mean and standard deviation of that particular data it might change to the next block. So, that means I can show you one example here you can see suppose this data if you think about this particular stationary data and if you take this block say this month or this block and if you take the calculate the mean and standard deviation and if you take this block say and if you calculate the mean and standard deviation probably almost it will remain same. So, this is stationary data, but so it is easier to make forecast for the future, but if you think about non-stationary data non-stationary data here if you see let us calculate the mean here suppose mean is falling on at that particular location and standard deviation here by suppose with the corresponding standard deviation and you could take this block say and we calculate the mean suppose your mean is coming here. So, you can see that in that case you can see the mean has shifted actually. So, therefore, this data now next you do not know where it will go it might come this way this way this way it can be anything right. So, therefore, what I am trying to say is that non-stationary data is to some extent unstable or too much of irregularity will be there randomness will come into the picture. So, it is very difficult to make a forecast. So, does it mean that we cannot make a forecast for non-stationary data in practice there might be many examples which falls under non-stationary data. So, in that case to some extent even stock price also falls under non-stationary category. So, how to handle that type of data? So, there is a way different models are there which we will discuss like Arima models are there, differencing process are there, you can use Dickey Fuller test and you can change the non-stationary data into stationary and you can make a you can bring AR model Arima model or say different type of whole-to-inter models which can take takes care of train and analyze the seasonality and decomposition method is also there. So, we will discuss all these models which can handle to some extent non-stationary data, but in general stationary data are we quite popular to as a part of time series data analysis because you know if you can bring moving average methods or exponential smoothing methods these are actually easy are quite suitable for stationary data and make a better forecast. So, we will discuss that type of both type of data and both type of analysis with suitable or appropriate time series models for this or methods for this particular data set whether it could be time say stationary data whether it could be non-stationary data. Now, come to the different components I have already given the introduction now actually the time series data are being classified into four category in general there might be many more aspects like you can think when the data will have a different type of randomness or irregularity it might follow in a different aspects or different pattern. So, therefore in general we classify the data into four category one is the trend then second is the cyclical and then seasonality and randomness or irregularities. I will discuss all these four with examples how the time series data actually behave over a period of time and how we can make a forecast for the future and all this depends on your how you are measuring the how are calculating the measure of accuracy or the error you are calculating of the data and accordingly you can make a forecast with accuracy level that means if the data has a trend you have to calculate the measure of accuracy and then the corresponding model you call and you run the model and then you see how much is the forecast and how much is the measure of accuracy for this data which is having trend. So, this way you have to come you have to make a forecast for the future. Let us see different type of frame of the data that means time frame that means data can be one month data it could be you know one year data 10 years data it can be in a couple of days data. Suppose you want to make a forecast for a stock price for say you know for next 10 days or next two three days and you have a last 10 days data. So, in that case you have only 10 days data you can consider is a short term data and you want to make a forecast for the you know next three days seven days or for two days you want to study the trend. So, in that case you can say it is a short term prediction maybe one or two period or maybe five six periods you want to make a forecast. So, that to some extent we call it a short term predictions even if you can think about the for intraday trading etc. it is like one hour two hours you want to make forecast right now what could happen in the next one hour. So, that you are trying to swing you want to swing of the stock price you want to capture. So, in that case it is a very short term predictions there is a model there are different models will study on this type of models for what period you want to make forecast for two period for three period for short term for long term for medium term. Suppose you have a quarterly data right as I mentioned in India generally data are being forecast or being calculated quarterly basis for any company whether it is a you know chemical sector whether it is a paint sector whether it is a you know automobile sector whether it is a IT sector or service sector financial sector everywhere generally on consumption FMCG sector generally company comes with their quarterly basis data. So, in that case when you have a quarterly basis data you want to make forecast for at least for one year. So, you have a four quarter. So, you can consider it as a like to some extent medium term prediction four to five quarter you want to make prediction or maybe next two years you want to make predictions. So, it can be considered as a medium term predictions or there could be examples where you need to make a forecast for next suppose Indian GDP you want to calculate right and you want to make a forecast or say you know population growth you want to forecast for 2030. So, this way you want to make prediction. So, in that case it is a long term prediction. So, you can have a previous data with longer duration of data that data you should also you should require you know long term data historical data you require that also long term and you want to make a forecast for long term also that very good actually suppose you want to calculate predict the you know trend of the Indian passengers which traveling through airlines. So, you want to make a prediction right. So, you need one you cannot have this with one year or two year of data there might be COVID cases where people have not developed that like airlines etc. So, that randomness regularly you have to remove then you have to go back to the past data. So, in that case you might have a longer period you might require a longer period of data and once you get that longer period of data you can make a better prediction for a longer period also maybe next five year six year what could be the trend of different airlines or different airport that you can make a better forecast. So, this way you can classify the data into short term prediction mid term medium term prediction and long term predictions and there is you know specific you know bar or you know demarcation that you this data or this period means it is short term it is a long term it is depending on the context of the problem depending on the availability of historical data and depending on the requirement of forecast. So, all this depends on the situation I have already discussed in one slide remember that different type of selection of a models and around eight points I have discussed. So, that is very important that slides and this is a actually you know one of the part the time frame of understanding is very important. So, you might have a data and you want to make a forecast for longer period and the data says that it is not possible to make a forecast you can make forecast, but that forecast may not be suitable or may might have huge amount of error it will not match with the reality. So, how to make a forecast for longer period which is not suitable with the existing data. So, in that case you might think about prediction of a short period one more example I can give to you suppose you want to make a prediction of air quality right. So, air quality you might need to prediction for say you know maybe now it is say 3 o'clock suppose and you want to make forecast for 4 o'clock. So, just one or 5 o'clock you want to make forecast. So, you have the data 2 o'clock 1 o'clock and 2 o'clock and say now you have to make forecast for say 3 o'clock or 4 o'clock or 5 o'clock. So, these type of you know short term data you might have and short term prediction it might also require. So, in that case it is a short term predictions. So, depending on the situation you have to make forecast and the data requirement you need to collect. Now, once you understand the requirement of the company or requirement of the forecast whether it is a short term, long term or medium term then the next part is that understand the data pattern because you have time frame you have decided whether the availability of the historical data and the requirement for the forecast. So, once you identify the time frame from both side for backside and the forthcoming side. So, once you know all these things then you understand the data behavior that historical data is been provided to you you take that data and make a forecast for them. Here are the top 4 you know most popular or majorly defined components of time series are that trend, seasonality, randomness or irregularities and cyclical. All 4 I am going to discuss today with examples. This is very important slides and very important for you know understanding of time series data and making bringing or selecting appropriate time series model. So, if you know better about this 4 pattern of the data the suitable model you can actually select. So, first trend, trend by term itself you can understand the trend means it is to some extent you know data maybe there might be up trend of the data there might be down trend of the data. So, if the data is following suppose sales are going up every year year on year basis or quarter to quarter basis. So, if the sales are up so that data is a trend data it is not steady data right it is not a stationary data. Suppose data is following like this over a period of time data is following like this or stock price is going up or you know sales of a company or the demand are going up. So, if you think that type of data it is to some extent non-stationary data. So, here if you calculate the mean your mean at in this time period your mean might be here, but at that time period if you come here in this from this block to that block you can see your mean might be here. So, mean has shifted actually. So, in that case we can say that this data non-stationary data, but this data is following trend. If this type of data are with you and if you realize that the or after drawing the graph if you realize that this data is following a trend specific trend may be up trend or down trend. So, which model to select. So, once you know one model suppose hold model once you know one model you bring that model and use this data for that particular model. And you will see a very good forecast as compared to the say moving average model which we will discuss in the next session. So, you will see the difference or measure of accuracy of the data where you use a appropriate model or if you use a wrong model. So, therefore, data understanding or component understanding of time series data is extremely important. So, this is all about trend part common sense you can use and you can understand that. Now, second is the seasonality. In India as I mentioned most of the companies predictions or the sales or you know demand and all these things production are generally been followed seasonality basis. It seasonal can seasonality can be you know monthly basis quarterly basis it can be you know like in India you can think about say summer season we can think about monson season winter season festival season we can classify this way also. We can classify seasonality in terms of quarter to quarter basic quarter one quarter two quarter three quarter four. For example, in India quarter one means say April 2 once the financial year starts. So, April May June. So, first quarter these three months are being clocked and we call it as a first quarter and then for say beverage products cold drinks products they have a higher sale in this particular month because it is a summer and then if you then suppose next quarter say quarter 2 suppose July August September. So, if you consider that quarter second quarter you might see the agrochemical products or say you know fertilizer products will have a higher sales. But that sales might not last in the says fourth quarter because once the crops are being harvested they people might not buy agrochemicals or fertilizer. So, therefore the our say even cold drinks products might not be bought in winter. So, therefore the product follows seasonality. So, if the seasonality pattern are involved in the data. So, once I understand the pattern of the data through graphs or through analyzing the past data. So, you have to bring the appropriate model for that. So, therefore, seasonality is one of the important components of the data. Here you can see the graph the blue the sky color graph this data in every year in a term period it is actually follows seasonality. Here you can see this year one year and if you see this time period at that time period there is a speak you go to the next year you can see at the same time period tentatively there is a high high sales. So, every year there is a specific pattern it is following. So, we call it as a seasonality that means it will be repeated the pattern is known and pattern will be repeated. So, it is easier to forecast despite it is a zigzag, but it is easier to forecast because the data pattern you know and it is following a similar pattern it will be repeated every year that pattern and in a particular time period might be sales might be high or low, but overall if you compare quarter to quarter basis or you know adjusted quarter you might see the sale is up or down. So, that pattern will be followed every year quarter to quarter basis and in that case that pattern is called as a seasonality. So, in that case how to handle the seasonality you have to calculate the seasonal index you have to do the scene decentralization of the data like you know you have to remove the seasonal factors and by by calculating the seasonal index or every weightage for a particular index and then you have to bring the appropriate model say winter model or so your decomposition method and then you can make a better forecast.  So, this randomness are not been there present in second year or in third year, but here it you can see the randomness is there. So, therefore, we call it as a randomness or irregularities I can give one more example. Suppose you know Middle East suppose Israel and Palestine were started right began. So, because of that there might be a crude oil price crude oil price may go up high, but after 3-4 months it may fall down. So, this crude oil in India crude oil price if it goes up it means that for that time period for this particular 2023 the crude oil price has gone up suppose and next year it might not go up at that time period because that is a specific event and because of that crude oil has gone up. So, this irregularity you have to remove this randomness you have to remove if you put that spike of crude oil price for your future forecast because it is not in it is not exist in the other years right or it might not come in the next year also or in the next year data it might not be there. So, therefore, this irregularities or randomness you have to remove then only you can make a better forecast for the future, but otherwise this outlayer will drag your forecast to upside or downsides. So, make sure of that. So, this is what randomness I can give one more example suppose during corona what happened once the corona came most of the you know like you know restaurant business or the tourism sector or hotel sector hotel business got closed right. So, or I mean startups also got affected heavily. So, what does it mean? Because of this one corona cases one or two year this particular industry or the sector say tourism or the hotel industry got suffered and there are no sale. So, you know generally there might be selling their sales are like this suddenly what happens one or two year it like this now they have come back to the normal mode, but if you want to make forecast for future in that case your forecast should follow like this, but if you include this particular data with this particular data of randomness or irregularities during covid period of one year or say two year. So, if you include that in the past data and if you do not replace that with the average data or you know to some extent interpolation. So, what happens you know your forecast might your forecast might come down because because of this irregularity or out layers it will or you know low data it might bring down your forecast, but actually it is not correct forecast your measure of accuracy which I am going to discuss now will be too high. So, you cannot consider this data this randomness. So, you have to replace with some average data then only or you can remove that data or you can replace this data then only you can get a better forecast with least RMSE or the error. So, that error will be considered is a better error. So, this is called randomness and it is heavily involved in time series data. In practical scenario if you go to different context and if you bring different industrial example you will or so real life social example you will find that randomness exist in the time series data and that data you cannot consider always without making a average or handling that in a or treating that with a separate aspects. So, therefore randomness has to be taken into a considered account in forecast and how to handle it how to replace it that will discuss later and that once you get that replacement of the data randomness or irregularities of the data or out layers then only you can make the focus on the data clean and the pre-processing are done then only you can think about forecasting for the future. Now, look at thing look at the last one before I go to the last one look at this particular example the graph these data actually includes trained signality and randomness look at the data the sky color data with this example here you can see this data has a signality look at this data has a uptrend also look at the blue color line this data has a irregularity randomness also. So, these three part that I have discussed so far are involved in this data and which is practical in reality and this type of data pattern you have to understand and there are components in to understand and then you have to make a forecast for the future with better accuracy if you want to keep a better accuracy in that case you require this type of component analysis and corresponding model you have to bring so that you can make a better forecast for example here this data look at this particular graph sky color graph this data has a signality as well as the trained right. So, when the data has a signality and trained which model is most suitable because you understood the data pattern and the behavior and you have classified the components also. So, you realize that the data has a signality and trained. So, you bring inter method or decomposition method which will be most suitable for that even hold model I told about for 10 data you may select the hold model right, but hold model is suitable for only trained data, but if the data has a signality and trained both then you cannot use hold model you can use but the measure of accuracy of the error will be too high. So, therefore you bring winter method or decomposition method which will discuss all these things later. So, this way you can actually understand or the data pattern through component analysis the past data behavior and you can make a better forecast with higher accuracy. Now, let us come to the last one that is called you know cyclical pattern. So, what is cyclical pattern? Cyclical pattern of data actually occurs for a longer duration that means it is not for short term or one year or two year. Suppose you know if government changes policy or so far as per my knowledge you know metal sector in India could not perform very well because you know because of government policy regulations etcetera or say edible oil sector sometimes are been totally you know government policy driven. So, therefore what happens they suffered now if you go also even airlines industry. So, they could not perform well most of the airlines are getting start shutdown or to some extent they are loss making they are in loss making business or they are selling their you know entire business actually. So, why? Because they are not performing very well. So, that might happen in last decade say, but now if government comes out PLI scheme or the incentive etcetera or subsidiary etcetera in that case you might see that the airline sector or say metal sector might perform well even I read a recent article that Jeffrey one of the like you know Bokharaj advisor or the consultant team they said that you know metal sector will perform very well in future in the coming years in India. What does it mean? Earlier last one decade or ten years they could not perform now they will perform because of government policy on the government initiatives. Suppose you can think about say you know defense sector India never perform very well defense in defense sector India is to you know import defense components from the foreign countries. Now India because of making India India is producing not only meeting the demand for Indian army and defense but also India is planning to make a export of you know aircraft of fighter jet etcetera or the defense components. What does it mean? It could not perform well but now it is working so it is a cyclical. Maybe ten years it will work then it might go down against some other sector will come forward. Because of China plus one policy what happens most of the clients are been shifting. Last two, three decades China actually dominated dominated based on that you know in manufacturing sector. But now because of China plus one policy or between the war environment or US China tracel what happens you know most of the companies are shifting from China to India. So it is an opportunity for India. Now think about chemical sectors most of the chemical sector are trying to you know semiconductor manufacturing sector are planning to set up their plan in India. So India will have an advantage for next one or two decade for them. So this is what cyclical think about another example of say you know Ukraine war, Russia Ukraine war what happens because of Russia Ukraine war you know the entire Europe is facing with energy crisis.  But after that they will come up with recovery. So this two years or three years period you can consider the cyclical pattern after that they will come back to the normal mode. So this type of whenever longer data longer suppose government changes policy with education policy or say population policy etc. population control policy. So in that case you might see that you know population will curve or say education policy once the changes you may say the rural area will get advantage of that. So this way if the data follow different pattern of a longer period and then it changes in the next two years three years or five years or one decade and then again changes so this type of data pattern follow cyclical. So cyclical data if you have and if you think that the data is having a longer period of trend or pattern and then it is changing for energy remaining steady for the next couple of years or decade and then again it might change. So this type of data pattern are called cyclical pattern. They don't fall in the trend they don't fall in the signality they don't fall in the randomness. So in that case this data you can analyze through cyclical pattern of the data and the corresponding forecast you can do. So these are the four category of you know time series data major four category one is the trend signality, randomness and cyclical. Now we will discuss the measure of accuracy. So let's take a 5 minutes break and then we will discuss the measure of accuracy for time series data.


So there are a lot of laws that happen that can be passed or not passed. Instead of what happens at the end of the day that are pending. I don't know how you should phrase that. So there are some that are, people who are just very young, but there are some that people get very, very excited about, and there are very strong feelings.  So what we're going to do now, before we get up, I want to tell you, I want to go out first. I want to go turn black. The government and all the governments are really moving along. And we're going to be like, are we going to talk more about the, are we really going to be like, are we going to be like this? Yes, I think more. There's going to be a lot of, you know, to be front-seeing or to talk to like, you know, to be like this. So we're going to see that today when we get out of here first, and we're going to get out of here first. So I know that we're going to get out of here first.