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Hey @Andrei Tulbure! The HistoricAverage model takes the entire history of the time series to produce forecasts. You could try using WindowAverage. With it, you can specify the

window_size

argument. For example,

WindowAverage(window_size=4)

will average the last 4 observations to produce forecasts.

And how are the forecasts produced? Is it a rolling forecast of the last 4 weeks? Or is it just a single value spread out over the fcast horizon? I would like the forecast to be rolling, like to always take into account also the last forecasted value when it makes the average for the window.

I see. Currently, an autoregressive window average is not supported. But both models (including the exponential moving average) are special cases of an

AR(p)

model with predefined coefficients (

1/p

in the case of the autoregressive window average). We are extending the

ARIMA

family to include the

AR

model separately, and we will have the option to specify the coefficients. Please help us open an issue on the statsforecast repo. 🙂

Okay, will do that. And also will fill in an issue