Custom Forecasters and Transformers

This guide shows you how to use functime's primitives to create new forecasters and transformers. If you would like to add your custom implementation into the functime library, please open up an draft pull request on GitHub! All contributions are welcome.

Build your own forecaster

🚧 Under construction.

Build your own transformer

functime provides an easy-to-use and functional @transformer decorator to implement new transformers. Here is an example:

@transformer
def lag(lags: List[int]):
    """Applies lag transformation to a LazyFrame.

    Parameters
    ----------
    lags : List[int]
        A list of lag values to apply.
    """

    def transform(X: pl.LazyFrame) -> pl.LazyFrame:
        entity_col = X.columns[0]
        time_col = X.columns[1]
        max_lag = max(lags)
        lagged_series = [
            (
                pl.all()
                .exclude([entity_col, time_col])
                .shift(lag)
                .over(entity_col)
                .name.suffix(f"__lag_{lag}")
            )
            for lag in lags
        ]
        X_new = (
            # Pre-sorting seems to improve performance by ~20%
            X.sort(by=[entity_col, time_col])
            .select(
                pl.col(entity_col).set_sorted(),
                pl.col(time_col).set_sorted(),
                *lagged_series,
            )
            .group_by(entity_col)
            .agg(pl.all().slice(max_lag))
            .explode(pl.all().exclude(entity_col))
        )
        artifacts = {"X_new": X_new}
        return artifacts

    return transform

Key points to note:

1. Specify all parameters in the outer function.
2. Implement a curried transform function inside the outer function that returns a dictionary. This dictionary must contain X_new key mapped to the transformed DataFrame. Every transform function expects a panel DataFrame.