i have a weekly aggregated data set and i splitted it in 80% Train and 20% Test. I am performing a one step a head forecast. However as the length becomes larger the performance get really bad. Is that Normal? The first few steps are predicted okayish. Plot Description
This seems like normal behavior. The forecasts of your model are based on the last observations of your time series. Let us say as an example, the forecast for October 1st is based on the values from September. Now, for October 1st you have 30 correct values from September. Depending on how good your model is, the prediction will deviate a little bit from the true value. When you predict October 2nd, you use 29 correct values from September and the previously predicted value for October 1st. This is called dynamic forecasting. The forecasting error from this prediction is now fed into the model, which impacts the quality of the forecast for October 2nd. If you predict October 3rd, you have 28 correct input values and already two with a slight deviation.
The values for November will be based on forecasts entirely and all forecasting errors in October are fed into the model. Your predictions for July are based on predictions that were based on predictions and so on. Therefore, the predictions get worse and worse the further you forecast into the future and at some points they tend to converge into a straight line.
For this reason, you cannot really predict that far into the future very often. 65 steps is very far into the future and I guess that is too much for ARIMA/VAR.
