![]() For one, some of the point forecasts are negative, which you presumably do not want. Whether they are more accurate is doubtful. Now the forecasts definitely look more, ahem, sophisticated. > plot(forecast(model_sarima,h=100),las=1)Īs you see, we now have a quite different (and seasonal) model. Sigma^2 estimated as 116.5: log likelihood=-686.48 ![]() Here is what happens then: > model_sarima model_sarima Use D=1 to force a seasonal model (see Seasonality not taken account of in auto.arima()). Note that the parameter seasonal=TRUE does not force auto.arima() to use a seasonal model, it only allows it. "Forecasting with long seasonal periods" by Rob Hyndman is very enlightening reading. ![]() I dput() the data at the bottom in case someone wants to take a look.ĪRIMA has a hard time dealing with "long" seasonality, especially if you have only observed two seasonal cycles. ![]()
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