NSSDNResult.forecast#

NSSDNResult.forecast(steps=1, *, exog=None, time=None)[source]#

Recursive multi-step forecast from the end of the fitted sample.

The network is warmed up on the sample under teacher forcing, the last observation is fed as the first future input, and every later input is the previous emission. Error accumulates with the horizon; the validation notebook found this mode far weaker than the conditional one-step mode under the filtered alignment.

Parameters:

  • steps (int) – Periods ahead, at least one.

  • exog (ArrayLike | None) – (steps, p) future covariates; required when the fit used them.

  • time (ArrayLike | None) – (steps,) future grid after time[-1]; defaults to continuing at the sample’s last step size.

Return type:

NDArray[double]

Returns:

(steps,) forecasts on the original scale.

Raises:
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