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I built a model for time series in order to forecast new values.
What is the best way to choose the correct model?
Are better criteria like AIC and BIC or the comparison between prediction errors? In this last case I have to split dataset in train and test while in the first case is not necessary, reducing time of implementation.
Thanks!
First thing first, when ever you use Time Series data you call it as Forecasting not Prediction as it is time dependent. To understand why you can go through this link
When you are trying to compare between models you need to use AIC,BIC, AUC etc values. you can go though this link to understand better
When you are accessing the performance of the model then you need to check for Error Rates(RMSE, MAE, MAPE, MSE etc). Yes, in this case you need to divide the data into Train and Test to access the model. You can go through this link for better understanding
To take it to the next level, you can use an ensemble to get better the result. This might or might not decrease the error rate. In most of the cases it is helpful. You can combine 2/3 moderately performing hotels outcome to get best results i.e., Ensemble Model.
