automl.time_series.sklearn

make_lag_features

def make_lag_features(X: pd.DataFrame, y: pd.Series, lags: int)

Transform input data X, y into autoregressive form by creating lags columns.

This function is called automatically by FLAML during the training process to convert time series data into a format suitable for sklearn-based regression models (e.g., lgbm, rf, xgboost). Users do NOT need to manually call this function or create lagged features themselves.

Parameters

X : pandas.DataFrame Input feature DataFrame, which may contain temporal features and/or exogenous variables.

y : array_like, (1d) Target vector (time series values to forecast).

lags : int Number of lagged time steps to use as features.

Returns

pandas.DataFrame Shifted dataframe with lags columns for each original feature. The target variable y is also lagged to prevent data leakage (i.e., we use y(t-1), y(t-2), ..., y(t-lags) to predict y(t)).

SklearnWrapper Objects

class SklearnWrapper()

Wrapper class for using sklearn-based models for time series forecasting.

This wrapper automatically handles the transformation of time series data into a supervised learning format by creating lagged features. It trains separate models for each step in the forecast horizon.

Users typically don't interact with this class directly - it's used internally by FLAML when sklearn-based estimators (lgbm, rf, xgboost, etc.) are selected for time series forecasting tasks.