stl_decomposer

Component that removes trends and seasonality from time series using STL.

Module Contents

Classes Summary

STLDecomposer

Removes trends and seasonality from time series using the STL algorithm.

Contents

class evalml.pipelines.components.transformers.preprocessing.stl_decomposer.STLDecomposer(time_index: str = None, degree: int = 1, seasonal_period: int = 7, random_seed: int = 0, **kwargs)

Removes trends and seasonality from time series using the STL algorithm.

https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html

Parameters

• time_index (str) – Specifies the name of the column in X that provides the datetime objects. Defaults to None.

• degree (int) – Not currently used. STL 3x “degree-like” values. None are able to be set at this time. Defaults to 1.

• seasonal_period (int) – The number of entries in the time series data that corresponds to one period of a cyclic signal. For instance, if data is known to possess a weekly seasonal signal, and if the data is daily data, seasonal_period should be 7. For daily data with a yearly seasonal signal, seasonal_period should be 365. For compatibility with the underlying STL algorithm, must be odd. If an even number is provided, the next, highest odd number will be used. Defaults to 7.

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	None
modifies_features	False
modifies_target	True
name	STL Decomposer
needs_fitting	True
training_only	False

Methods

clone	Constructs a new component with the same parameters and random state.
default_parameters	Returns the default parameters for this component.
describe	Describe a component and its parameters.
determine_periodicity	Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal.
fit	Fits the STLDecomposer and determine the seasonal signal.
fit_transform	Removes fitted trend and seasonality from target variable.
get_trend_dataframe	Return a list of dataframes with 4 columns: signal, trend, seasonality, residual.
inverse_transform	Adds back fitted trend and seasonality to target variable.
load	Loads component at file path.
parameters	Returns the parameters which were used to initialize the component.
plot_decomposition	Plots the decomposition of the target signal.
save	Saves component at file path.
set_seasonal_period	Function to set the component's seasonal period based on the target's seasonality.
transform	Transforms the target data by removing the STL trend and seasonality.

clone(self)

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)

Describe a component and its parameters.

Parameters

• print_name (bool, optional) – whether to print name of component

• return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

determine_periodicity(self, X: pandas.DataFrame, y: pandas.Series, method: str = 'autocorrelation')

Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal.

Parameters

• X (pandas.DataFrame) – The feature data of the time series problem.

• y (pandas.Series) – The target data of a time series problem.

• method (str) – Either “autocorrelation” or “partial-autocorrelation”. The method by which to determine the first period of the seasonal part of the target signal. “partial-autocorrelation” should currently not be used. Defaults to “autocorrelation”.

Returns

The integer numbers of entries in time series data over which the seasonal part of the target data

repeats. If the time series data is in days, then this is the number of days that it takes the target’s seasonal signal to repeat. Note: the target data can contain multiple seasonal signals. This function will only return the first, and thus, shortest period. E.g. if the target has both weekly and yearly seasonality, the function will only return “7” and not return “365”. If no period is detected, returns [None].

Return type

(list[int])

fit(self, X: pandas.DataFrame, y: pandas.Series = None) → STLDecomposer

Fits the STLDecomposer and determine the seasonal signal.

Instantiates a statsmodels STL decompose object with the component’s stored parameters and fits it. Since the statsmodels object does not fit the sklearn api, it is not saved during __init__() in _component_obj and will be re-instantiated each time fit is called.

To emulate the sklearn API, when the STL decomposer is fit, the full seasonal component, a single period sample of the seasonal component, the full trend-cycle component and the residual are saved.

y(t) = S(t) + T(t) + R(t)

Parameters

• X (pd.DataFrame, optional) – Conditionally used to build datetime index.

• y (pd.Series) – Target variable to detrend and deseasonalize.

Returns

self

Raises

• ValueError – If y is None.

• ValueError – If target data doesn’t have DatetimeIndex AND no Datetime features in features data

fit_transform(self, X: pandas.DataFrame, y: pandas.Series = None) → tuple[pandas.DataFrame, pandas.Series]

Removes fitted trend and seasonality from target variable.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable to detrend and deseasonalize.

Returns

The first element are the input features returned without modification.

The second element is the target variable y with the fitted trend removed.

Return type

tuple of pd.DataFrame, pd.Series

get_trend_dataframe(self, X, y)

Return a list of dataframes with 4 columns: signal, trend, seasonality, residual.

Parameters

• X (pd.DataFrame) – Input data with time series data in index.

• y (pd.Series or pd.DataFrame) – Target variable data provided as a Series for univariate problems or a DataFrame for multivariate problems.

Returns

Each DataFrame contains the columns “signal”, “trend”, “seasonality” and “residual,”

with the latter 3 column values being the decomposed elements of the target data. The “signal” column is simply the input target signal but reindexed with a datetime index to match the input features.

Return type

list of pd.DataFrame

Raises

• TypeError – If X does not have time-series data in the index.

• ValueError – If time series index of X does not have an inferred frequency.

• ValueError – If the forecaster associated with the detrender has not been fit yet.

• TypeError – If y is not provided as a pandas Series or DataFrame.

inverse_transform(self, y_t: pandas.Series) → tuple[pandas.DataFrame, pandas.Series]

Adds back fitted trend and seasonality to target variable.

The STL trend is projected to cover the entire requested target range, then added back into the signal. Then, the seasonality is projected forward to and added back into the signal.

Parameters

y_t (pd.Series) – Target variable.

Returns

The first element are the input features returned without modification.

The second element is the target variable y with the trend and seasonality added back in.

Return type

tuple of pd.DataFrame, pd.Series

Raises

ValueError – If y is None.

static load(file_path)

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

property parameters(self)

Returns the parameters which were used to initialize the component.

plot_decomposition(self, X: pandas.DataFrame, y: pandas.Series, show: bool = False) → tuple[matplotlib.pyplot.Figure, list]

Plots the decomposition of the target signal.

Parameters

• X (pd.DataFrame) – Input data with time series data in index.

• y (pd.Series or pd.DataFrame) – Target variable data provided as a Series for univariate problems or a DataFrame for multivariate problems.

• show (bool) – Whether to display the plot or not. Defaults to False.

Returns

The figure and axes that have the decompositions

plotted on them

Return type

matplotlib.pyplot.Figure, list[matplotlib.pyplot.Axes]

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)

Saves component at file path.

Parameters

• file_path (str) – Location to save file.

• pickle_protocol (int) – The pickle data stream format.

set_seasonal_period(self, X: pandas.DataFrame, y: pandas.Series)

Function to set the component’s seasonal period based on the target’s seasonality.

Parameters

• X (pandas.DataFrame) – The feature data of the time series problem.

• y (pandas.Series) – The target data of a time series problem.

transform(self, X: pandas.DataFrame, y: pandas.Series = None) → tuple[pandas.DataFrame, pandas.Series]

Transforms the target data by removing the STL trend and seasonality.

Uses an ARIMA model to project forward the addititve trend and removes it. Then, utilizes the first period’s worth of seasonal data determined in the .fit() function to extrapolate the seasonal signal of the data to be transformed. This seasonal signal is also assumed to be additive and is removed.

Parameters

• X (pd.DataFrame, optional) – Conditionally used to build datetime index.

• y (pd.Series) – Target variable to detrend and deseasonalize.

Returns

The input features are returned without modification. The target

variable y is detrended and deseasonalized.

Return type

tuple of pd.DataFrame, pd.Series

Raises

ValueError – If target data doesn’t have DatetimeIndex AND no Datetime features in features data