Time series Analysis in R
<p>I have been given a dataset for 118 days. I'm supposed to forecast the values for the next 28 days. I've tried out the below code. But I'm getting the same values for all the 28 days. Can you help me find my mistake? Thank you. </p>

<pre><code>library(forecast)
library(dplyr)
head(product)
ts_product = ts(product$Qty, start=1,frequency=1)
ts_product
plot(ts_product)
#predictions of 28 days
m_ets = ets(ts_product)
f_ets = forecast(m_ets, h=28)
plot(f_ets)
</code></pre>

<p>The data for <code>Qty</code> is given by:</p>

<blockquote>
  <p>Qty = c(53, 40, 37, 45, 69, 105, 62, 101, 104, 46, 92, 157, 133, 173, 
  139, 163, 145, 154, 245, 147, 85, 131, 228, 192, 240, 346, 267, 
  267, 243, 233, 233, 244, 241, 136, 309, 236, 310, 266, 280, 321, 
  349, 335, 410, 226, 391, 314, 250, 368, 282, 203, 250, 233, 233, 
  277, 338, 279, 279, 266, 253, 178, 238, 126, 279, 258, 350, 277, 
  226, 287, 180, 268, 191, 279, 214, 133, 292, 212, 307, 232, 165, 
  107, 121, 188, 198, 154, 128, 85, 106, 67, 63, 88, 107, 56, 41, 
  59, 27, 58, 80, 75, 93, 54, 14, 36, 107, 82, 83, 112, 37, 57, 
  9, 51, 47, 57, 68, 97, 25, 45, 69, 89)</p>
</blockquote>

<p>This is the prediction I get.</p>

<pre><code>Point Forecast      Lo 80    Hi 80      Lo 95    Hi 95
119       69.53429   2.089823 136.9788  -33.61312 172.6817
120       69.53429  -2.569107 141.6377  -40.73834 179.8069
121       69.53429  -6.944751 146.0133  -47.43031 186.4989
122       69.53429 -11.083248 150.1518  -53.75959 192.8282
123       69.53429 -15.019428 154.0880  -59.77946 198.8480
124       69.53429 -18.780346 157.8489  -65.53129 204.5999
125       69.53429 -22.387517 161.4561  -71.04798 210.1166
126       69.53429 -25.858385 164.9270  -76.35622 215.4248
127       69.53429 -29.207323 168.2759  -81.47798 220.5466
128       69.53429 -32.446345 171.5149  -86.43163 225.5002
129       69.53429 -35.585612 174.6542  -91.23273 230.3013
130       69.53429 -38.633808 177.7024  -95.89454 234.9631
131       69.53429 -41.598429 180.6670 -100.42854 239.4971
132       69.53429 -44.485993 183.5546 -104.84468 243.9133
133       69.53429 -47.302214 186.3708 -109.15172 248.2203
134       69.53429 -50.052133 189.1207 -113.35736 252.4259
135       69.53429 -52.740222 191.8088 -117.46844 256.5370
136       69.53429 -55.370474 194.4391 -121.49106 260.5596
137       69.53429 -57.946468 197.0150 -125.43070 264.4993
138       69.53429 -60.471431 199.5400 -129.29230 268.3609
139       69.53429 -62.948280 202.0169 -133.08032 272.1489
140       69.53429 -65.379664 204.4482 -136.79880 275.8674
141       69.53429 -67.768000 206.8366 -140.45144 279.5200
142       69.53429 -70.115495 209.1841 -144.04163 283.1102
143       69.53429 -72.424177 211.4928 -147.57245 286.6410
144       69.53429 -74.695908 213.7645 -151.04676 290.1153
145       69.53429 -76.932409 216.0010 -154.46719 293.5358
146       69.53429 -79.135268 218.2038 -157.83618 296.9048
</code></pre>

<p>Also, do you think any other model other than ets, which we have used here will work for this problem ?</p>