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I have a time series going from 2013 until late 2016. I am using the auto.arima function in R to forecast the next 12 months. I get the following where the black line are my observations and the blue line is my prediction.

fcast <- forecast(auto.arima(a.ts))
plot(forecast(fcast, h = 12))

arima forecast

However when I use this custom arima function I get a much better result

fit <- arima(a.ts,seasonal=list(order=c(0,1,0),period=12))
plot(forecast(fcast, h = 12))

custom arima

Why is the auto.arima function not producing a result that doesn't seem anywhere near as good. I know they are using different parameters but I would have thought that auto.arima would have found the best parameters and forecasted using those?

kjetil b halvorsen
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filod
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    When things don't meet your expectations then you have to reduce your expectations or use other "things" . – IrishStat Oct 29 '16 at 21:59
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    Did you specify the frequency of the time series for `auto.arima`? – Scortchi - Reinstate Monica Oct 30 '16 at 00:17
  • There's not really enough information even for people familiar with `auto.arima` to do more than guess what the issue is here: if it's not merely that the seasonal `period` argument needs to be supplied, please edit the question to give more detail & it can of course be re-opened. [Auto.arima with daily data: how to capture seasonality/periodicity?](http://stats.stackexchange.com/q/14742/17230) may also be helpful. – Scortchi - Reinstate Monica Nov 01 '16 at 14:20

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