I have a number of daily time series to be forecasted for a horizon of one week, i.e. 7 days, in an online, automated way. A lot of times the series change due to some exogenous factors that I cannot control. What would be a good way of combining a change point detection algorithm with a forecasting technique to update my forecasts?
The first thing that came into my mind is once the change has been detected, keep the previous forecast for some time until enough data are available and then retrain the model on the new data, discarding all the previous ones.