Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA. Now, the purpose is to find out which model performs best on a given use case with different time horizons.
The time series data is aggregated in weekly time-stamps.
A train-test split of 80/20 percent is performed. The question is: Which model is best for a 1 month, 6 months and 12 Months forecast?
How to evaluate those with RMSE?
Should the testset size change everytime? For example, use the 80% training data to train and predict on
testdata[:30] and so on. Is there an other methology?
A simple AR Model and it seems like it is doing worse with a
testdata[:10] prediction than with the whole testdata set.