Closed ha0ye closed 4 years ago
After digging into the built-in arima()
function and autoarima()
and arfima()
(from the forecast
package, designed to operate similarly), the procedure is a bit more constrained.
These functions are designed to forecast forward in time only (and not as generic models for {state at time t
} -> {state at time t+1
}). So trying to re-use a fitted model and apply it to different portions of the time series is not really doable. (I suspect this is because of how Kalman filter and similar are operating under the hood that estimate true state vs. observations.)
So instead of trying to do cross-validation or use a static model for multiple forecasts, I'm moving over to a hindcasting approach:
We can use a parameter for when to start hindcasting. This will support later tests of how much data is needed, relative to model selection, etc.
Objective:
Procedure:
Datasets:
Predictors: