I am using the forecast package in R to generate an ARIMA model for my data. I started with the auto.arima function for a try and got a ARIMA(1,1,2) model.
ar1 ma1 ma2 0.7734 -1.0773 0.1191 s.e. 0.0709 0.0962 0.0824
But my question is not about the model itself but more about the validation of the model accuracy in general. Therefore I used "accuracy(fit)" and obtained the following output:
ME RMSE MAE MPE MAPE MASE ACF1 Training set -1.580214 163.8034 94.91732 -4.18724 13.61585 1.029359 0.002118006
I interpreted the MAPE like, "on average, the forecast if off by 14%", which sounds fine for me. But on the other side the MASE is greater than 1, which means the model is worse than a naive model. (?)
When I plot a forecast obviously the model is really bad:
fc<-forecast(fit, level=80, h=100) plot(fc)
I am confused about the interpretation of the accuracy parameters I got. Did I miss some R command or how can I interpret the accuracy? Thanks!