I have trained ARIMA model in Python (concretely SARIMAX, as I need seasonal ARIMA). That gives me a model which I can use to forecast one or multiple future values.
What would be the correct procedure to create real time predictions? Should I:
- retrain ARIMA model each time a new value occurs and forecast one value
- retrain ARIMA model after N moments and predict values for next N moments
- something else?
Also, if I retrain model should I:
- use all previous values
- use last N values
If I should use only last N values how to determine size of N?
I'm familiar with concept that you generate a model periodically (but not too often) and use that model on real time values to predict the dependent variable, but in case of time series it seems that retraining a model is needed more often to get better predictions.