And here is what I've meant for Expanding window cross_validation (in statsforecast you can set the

step_size

, which is missing from mlforecast) Is there a method to do Expanding window cross_validation in mlforecast too?

@Maria @Andrei Tulbure

As far as I reckon modifying the

offset

variable from the

backtest_splits()

function did what I wanted (Expanding window with

step_size=1

) Previous:

def backtest_splits(
    data,
    n_windows: int,
    window_size: int,
    freq: Union[pd.offsets.BaseOffset, int],
    time_col: str = "ds",
):
    for i in range(n_windows):
        offset = (n_windows - i) * window_size
        ...

Now:

def backtest_splits(
    data,
    n_windows: int,
    window_size: int,
    freq: Union[pd.offsets.BaseOffset, int],
    time_col: str = "ds",
    expending_window = int,
):
    if expending_window == 1:

        for i in range(n_windows):
            offset = window_size+n_windows-1-i
            ... (the rest of the code is left unchanged)
    else:
        Previous implementation

Also I added expending_window as an argument in the cross_validation method from the MLForecast class like so:

def cross_validation(
        self,
        data: pd.DataFrame,
        n_windows: int,
        window_size: int,
        id_col: str,
        time_col: str,
        target_col: str,
        expending_window: int = 0,
        static_features: Optional[List[str]] = None,
        dropna: bool = True,
        keep_last_n: Optional[int] = None,
        dynamic_dfs: Optional[List[pd.DataFrame]] = None,
        predict_fn: Optional[Callable] = None,
        **predict_fn_kwargs,
    ):

If it's not specified it will use the previous implementation, if it's 1 it will use Expanding window with size of 1. Most probably not the best aproach but I needed it fast

Should i make a PR with the changes?

On the second point, although both evaluation techniques are valid, in practice it is preferred to calculate the loss on each fold separately and then average it (as in mlforecast). We will update the statsforecast documentation to reflect this. 🙌