v1.0.2

Bug Fixes

• fix(compat): handle zero offset in shift_array @jmoralez (#480)
• fix(global-sklearn-tfm): apply inverse transform to each column @jmoralez (#477)

v1.0.1

Bug Fixes

• fix: X_df handling in direct approach @jmoralez (#468)
• fix(auto): remove invalid params from xgboost default space @jmoralez (#464)

v1.0.0

Breaking Change

• breaking: remove window_ops and numba dependencies @jmoralez (#462)

v0.15.1

Changes

• chore: deprecate window_ops @jmoralez (#410)

New Features

• feat(distributed): support ids in predict @jmoralez (#454)
• feat(auto): support input_size @jmoralez (#451)

v0.15.0

Breaking Change

• breaking: drop rows with null targets when dropna=False @jmoralez (#447)

Bug Fixes

• fix(auto): support custom column names @jmoralez (#449)

Enhancement

• enh(distributed): propagate null features in spark @jmoralez (#448)

v0.14.0

New Features

• feat: add weight_col to MLForecast.fit and MLForecast.cross_validation @jmoralez (#444)
• feat: infer samples required for built-in lag transforms updates @jmoralez (#445)

v0.13.6

Bug Fixes

• fix(distributed): exogenous handling in distributed cross validation @jmoralez (#443)
• fix(distributed): support pre-computed features @jmoralez (#436)

v0.13.5

Enhancement

• enh: add step_size to AutoMLForecast @jmoralez (#426)
• support step_size selection in optimization.mlforecast_objective @bchaoss (#419)
• use TypeVar for dataframes and distribute py.typed file @jmoralez (#408)

v0.13.4

New Features

• feat: mlflow flavor @jmoralez (#406)

Documentation

• Clear up the README for the new user @Ammar-Azman (#397)

Enhancement

• make season_length optional in AutoMLForecast @jmoralez (#399)

v0.13.3

Bug Fixes

• handle no target transforms in DistributedMLForecast.to_local @jmoralez (#388)

Enhancement

• ensure static features are constant @jmoralez (#391)