{"action":"opened","issue":{"url":"https://api.github.com/repos/robjhyndman/forecast/issues/198","labels_url":"https://api.github.com/repos/robjhyndman/forecast/issues/198/labels{/name}","comments_url":"https://api.github.com/repos/robjhyndman/forecast/issues/198/comments","events_url":"https://api.github.com/repos/robjhyndman/forecast/issues/198/events","html_url":"https://github.com/robjhyndman/forecast/issues/198","id":122810938,"number":198,"title":"Variance of fitted tbats model is very high","user":{"login":"nelsonauner","id":3402261,"avatar_url":"https://avatars.githubusercontent.com/u/3402261?v=3","gravatar_id":"","url":"https://api.github.com/users/nelsonauner","html_url":"https://github.com/nelsonauner","followers_url":"https://api.github.com/users/nelsonauner/followers","following_url":"https://api.github.com/users/nelsonauner/following{/other_user}","gists_url":"https://api.github.com/users/nelsonauner/gists{/gist_id}","starred_url":"https://api.github.com/users/nelsonauner/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nelsonauner/subscriptions","organizations_url":"https://api.github.com/users/nelsonauner/orgs","repos_url":"https://api.github.com/users/nelsonauner/repos","events_url":"https://api.github.com/users/nelsonauner/events{/privacy}","received_events_url":"https://api.github.com/users/nelsonauner/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-12-17T19:54:40Z","updated_at":"2015-12-17T19:54:40Z","closed_at":null,"body":"Possibly related to an issue you closed: https://github.com/robjhyndman/forecast/issues/87\r\nOriginal SO report here: http://stackoverflow.com/questions/34333967/computational-instability-in-r-forecast-package\r\n\r\nWhen fitting a multiseasonal time series model with tbats, the variance of the fitted model is very high. \r\nIn addition, the fitted model varies if you use\r\n\r\n```R\r\nx.msts <- msts(series,seasonal.periods = 7)\r\nmodel_1 <- tbats(x.msts) #variance is 4.9e+17\r\nmodel_2 <- tbats( series, seasonal.periods = 7) #variance is 0.969\r\n```\r\n\r\nThis is still an issue if you have more than one seasonality factor :\r\n\r\n```R\r\nx2.msts <- msts(series,seasonal.periods = c(7,30))\r\nmodel_x2_1 <- tbats(x2.msts) #same high variance\r\nmodel_x2_2 <- tbats( series, seasonal.periods = c(7,30) ) #also high variance\r\n```\r\n\r\nI accept that I might not be using these models as they are intended to be used, but I thought it was possible that the intended result of using this code may not be these high variance forecasts"}}