I have a daily weather data set, which has, unsurprisingly, very strong seasonal effect.
I adapted an ARIMA model to this data set using the function auto.arima from forecast package. To my surprise the function does not apply any seasonal operations- seasonal differencing, seasonal ar or ma components. Here is the model it estimated:
library(forecast) data<-ts(data,frequency=365) auto.arima(Berlin) Series: data ARIMA(3,0,1) with non-zero mean Coefficients: ar1 ar2 ar3 ma1 intercept 1.7722 -0.9166 0.1412 -0.8487 283.0378 s.e. 0.0260 0.0326 0.0177 0.0214 1.7990 sigma^2 estimated as 5.56: log likelihood=-8313.74 AIC=16639.49 AICc=16639.51 BIC=16676.7
And also the forecasts using this model are not really satisfying. Here is the plot of the forecast:
Can anyone give me a hint what is wrong here?