`module` `mlforecast.target_transforms`

`class` `BaseTargetTransform`

Base class used for target transformations.

`method` `fit_transform`

fit_transform(
    df: Union[DataFrame, pl_DataFrame]
) → Union[DataFrame, pl_DataFrame]

`method` `inverse_transform`

inverse_transform(
    df: Union[DataFrame, pl_DataFrame]
) → Union[DataFrame, pl_DataFrame]

`method` `set_column_names`

set_column_names(id_col: str, time_col: str, target_col: str)

`method` `stack`

stack(
    transforms: Sequence[ForwardRef('BaseTargetTransform')]
) → BaseTargetTransform

`method` `update`

update(df: Union[DataFrame, pl_DataFrame]) → Union[DataFrame, pl_DataFrame]

`class` `Differences`

Subtracts previous values of the serie. Can be used to remove trend or seasonalities.

`method` `init`

__init__(differences: Iterable[int])

`method` `fit_transform`

fit_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform`

inverse_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform_fitted`

inverse_transform_fitted(ga: GroupedArray) → GroupedArray

`method` `set_num_threads`

set_num_threads(num_threads: int) → None

`method` `stack`

stack(scalers: Sequence[ForwardRef('Differences')]) → Differences

`method` `take`

take(idxs: ndarray) → Differences

`method` `update`

update(ga: GroupedArray) → GroupedArray

`class` `AutoDifferences`

Find and apply the optimal number of differences to each serie. Args:

max_diffs (int): Maximum number of differences to apply.

`method` `init`

__init__(max_diffs: int)

`method` `fit_transform`

fit_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform`

inverse_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform_fitted`

inverse_transform_fitted(ga: GroupedArray) → GroupedArray

`method` `set_num_threads`

set_num_threads(num_threads: int) → None

`method` `stack`

stack(
    scalers: Sequence[ForwardRef('_BaseGroupedArrayTargetTransform')]
) → _BaseGroupedArrayTargetTransform

`method` `take`

take(idxs: ndarray) → AutoDifferences

`method` `update`

update(ga: GroupedArray) → GroupedArray

`class` `AutoSeasonalDifferences`

Find and apply the optimal number of seasonal differences to each group. Args:

season_length (int): Length of the seasonal period.
max_diffs (int): Maximum number of differences to apply.
n_seasons (int, optional): Number of seasons to use to determine the number of differences. Defaults to 10. If None will use all samples, otherwise season_length * n_seasons samples will be used for the test. Smaller values will be faster but could be less accurate.

`method` `init`

__init__(season_length: int, max_diffs: int, n_seasons: Optional[int] = 10)

`method` `fit_transform`

fit_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform`

inverse_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform_fitted`

inverse_transform_fitted(ga: GroupedArray) → GroupedArray

`method` `set_num_threads`

set_num_threads(num_threads: int) → None

`method` `stack`

stack(
    scalers: Sequence[ForwardRef('_BaseGroupedArrayTargetTransform')]
) → _BaseGroupedArrayTargetTransform

`method` `take`

take(idxs: ndarray) → AutoDifferences

`method` `update`

update(ga: GroupedArray) → GroupedArray

`class` `AutoSeasonalityAndDifferences`

Find the length of the seasonal period and apply the optimal number of differences to each group. Args:

max_season_length (int): Maximum length of the seasonal period.
max_diffs (int): Maximum number of differences to apply.
n_seasons (int, optional): Number of seasons to use to determine the number of differences. Defaults to 10. If None will use all samples, otherwise max_season_length * n_seasons samples will be used for the test. Smaller values will be faster but could be less accurate.

`method` `init`

__init__(max_season_length: int, max_diffs: int, n_seasons: Optional[int] = 10)

`method` `fit_transform`

fit_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform`

inverse_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform_fitted`

inverse_transform_fitted(ga: GroupedArray) → GroupedArray

`method` `set_num_threads`

set_num_threads(num_threads: int) → None

`method` `stack`

stack(
    scalers: Sequence[ForwardRef('_BaseGroupedArrayTargetTransform')]
) → _BaseGroupedArrayTargetTransform

`method` `take`

take(idxs: ndarray) → AutoDifferences

`method` `update`

update(ga: GroupedArray) → GroupedArray

`class` `LocalStandardScaler`

Standardizes each serie by subtracting its mean and dividing by its standard deviation.

`method` `fit_transform`

fit_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform`

inverse_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform_fitted`

inverse_transform_fitted(ga: GroupedArray) → GroupedArray

`method` `set_num_threads`

set_num_threads(num_threads: int) → None

`method` `stack`

stack(
    scalers: Sequence[ForwardRef('_BaseGroupedArrayTargetTransform')]
) → _BaseGroupedArrayTargetTransform

`method` `take`

take(idxs: ndarray) → _BaseLocalScaler

`method` `update`

update(ga: GroupedArray) → GroupedArray

`class` `LocalMinMaxScaler`

Scales each serie to be in the [0, 1] interval.

`method` `fit_transform`

fit_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform`

inverse_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform_fitted`

inverse_transform_fitted(ga: GroupedArray) → GroupedArray

`method` `set_num_threads`

set_num_threads(num_threads: int) → None

`method` `stack`

stack(
    scalers: Sequence[ForwardRef('_BaseGroupedArrayTargetTransform')]
) → _BaseGroupedArrayTargetTransform

`method` `take`

take(idxs: ndarray) → _BaseLocalScaler

`method` `update`

update(ga: GroupedArray) → GroupedArray

`class` `LocalRobustScaler`

Scaler robust to outliers. Args:

scale (str): Statistic to use for scaling. Can be either ‘iqr’ (Inter Quartile Range) or ‘mad’ (Median Asbolute Deviation). Defaults to ‘iqr’.

`method` `init`

__init__(scale: str)

`method` `fit_transform`

fit_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform`

inverse_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform_fitted`

inverse_transform_fitted(ga: GroupedArray) → GroupedArray

`method` `set_num_threads`

set_num_threads(num_threads: int) → None

`method` `stack`

stack(
    scalers: Sequence[ForwardRef('_BaseGroupedArrayTargetTransform')]
) → _BaseGroupedArrayTargetTransform

`method` `take`

take(idxs: ndarray) → _BaseLocalScaler

`method` `update`

update(ga: GroupedArray) → GroupedArray

`class` `LocalBoxCox`

Finds the optimum lambda for each serie and applies the Box-Cox transformation

`method` `init`

__init__()

`method` `fit_transform`

fit_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform`

inverse_transform(ga: GroupedArray) → GroupedArray

`method` `inverse_transform_fitted`

inverse_transform_fitted(ga: GroupedArray) → GroupedArray

`method` `set_num_threads`

set_num_threads(num_threads: int) → None

`method` `stack`

stack(
    scalers: Sequence[ForwardRef('_BaseGroupedArrayTargetTransform')]
) → _BaseGroupedArrayTargetTransform

`method` `take`

take(idxs: ndarray) → _BaseLocalScaler

`method` `update`

update(ga: GroupedArray) → GroupedArray

`class` `GlobalSklearnTransformer`

Applies the same scikit-learn transformer to all series.

`method` `init`

__init__(transformer: TransformerMixin)

`method` `fit_transform`

fit_transform(
    df: Union[DataFrame, pl_DataFrame]
) → Union[DataFrame, pl_DataFrame]

`method` `inverse_transform`

inverse_transform(
    df: Union[DataFrame, pl_DataFrame]
) → Union[DataFrame, pl_DataFrame]

`method` `set_column_names`

set_column_names(id_col: str, time_col: str, target_col: str)

`method` `stack`

stack(
    transforms: Sequence[ForwardRef('GlobalSklearnTransformer')]
) → GlobalSklearnTransformer

`method` `update`

update(df: Union[DataFrame, pl_DataFrame]) → Union[DataFrame, pl_DataFrame]

Getting Started

How-to guides

Tutorials

API Reference

​module mlforecast.target_transforms

​class BaseTargetTransform

​method fit_transform

​method inverse_transform

​method set_column_names

​method stack

​method update

​class Differences

​method __init__

​method fit_transform

​method inverse_transform

​method inverse_transform_fitted

​method set_num_threads

​method stack

​method take

​method update

​class AutoDifferences

​method __init__

​method fit_transform

​method inverse_transform

​method inverse_transform_fitted

​method set_num_threads

​method stack

​method take

​method update

​class AutoSeasonalDifferences

​method __init__

​method fit_transform

​method inverse_transform

​method inverse_transform_fitted

​method set_num_threads

​method stack

​method take

​method update

​class AutoSeasonalityAndDifferences

​method __init__

​method fit_transform

​method inverse_transform

​method inverse_transform_fitted

​method set_num_threads

​method stack

​method take

​method update

​class LocalStandardScaler

​method fit_transform

​method inverse_transform

​method inverse_transform_fitted

​method set_num_threads

​method stack

​method take

​method update

​class LocalMinMaxScaler

​method fit_transform

​method inverse_transform

​method inverse_transform_fitted

​method set_num_threads

​method stack

​method take

​method update

​class LocalRobustScaler

​method __init__

​method fit_transform

​method inverse_transform

​method inverse_transform_fitted

​method set_num_threads

​method stack

​method take

​method update

​class LocalBoxCox

​method __init__

​method fit_transform

​method inverse_transform

​method inverse_transform_fitted

​method set_num_threads

​method stack

​method take

`module` `mlforecast.target_transforms`

`class` `BaseTargetTransform`

`method` `fit_transform`

`method` `inverse_transform`

`method` `set_column_names`

`method` `stack`

`method` `update`

`class` `Differences`

`method` `init`

`method` `fit_transform`

`method` `inverse_transform`

`method` `inverse_transform_fitted`

`method` `set_num_threads`

`method` `stack`

`method` `take`

`method` `update`

`class` `AutoDifferences`

`method` `init`

`method` `fit_transform`

`method` `inverse_transform`

`method` `inverse_transform_fitted`

`method` `set_num_threads`

`method` `stack`

`method` `take`

`method` `update`

`class` `AutoSeasonalDifferences`

`method` `init`

`method` `fit_transform`

`method` `inverse_transform`

`method` `inverse_transform_fitted`

`method` `set_num_threads`

`method` `stack`

`method` `take`

`method` `update`

`class` `AutoSeasonalityAndDifferences`

`method` `init`

`method` `fit_transform`

`method` `inverse_transform`

`method` `inverse_transform_fitted`

`method` `set_num_threads`

`method` `stack`

`method` `take`

`method` `update`

`class` `LocalStandardScaler`

`method` `fit_transform`

`method` `inverse_transform`

`method` `inverse_transform_fitted`

`method` `set_num_threads`

`method` `stack`

`method` `take`

`method` `update`

`class` `LocalMinMaxScaler`

`method` `fit_transform`

`method` `inverse_transform`

`method` `inverse_transform_fitted`

`method` `set_num_threads`

`method` `stack`

`method` `take`

`method` `update`

`class` `LocalRobustScaler`

`method` `init`

`method` `fit_transform`

`method` `inverse_transform`

`method` `inverse_transform_fitted`

`method` `set_num_threads`

`method` `stack`

`method` `take`

`method` `update`

`class` `LocalBoxCox`

`method` `init`

`method` `fit_transform`

`method` `inverse_transform`

`method` `inverse_transform_fitted`

`method` `set_num_threads`

`method` `stack`

`method` `take`

`method` `update`

`class` `GlobalSklearnTransformer`

`method` `init`

`method` `fit_transform`