gtime.causality.ShiftedPearsonCorrelation

class gtime.causality.ShiftedPearsonCorrelation(min_shift: int = 1, max_shift: int = 10, target_col: str = 'y', dropna: bool = False, bootstrap_iterations: int = None)

Class responsible for assessing the shifted Pearson correlations (PPMCC) between two or more series. For more info about the test, click here.

Parameters

min_shiftint, optional, default: 1

The minimum number of shifts to check for.

max_shiftint, optional, default: 10

The maximum number of shifts to check for.

target_colstr, optional, default: 'y'

The column to use as the a reference (i.e., the columns which is not shifted).

dropnabool, optional, default: False

Determines if the Nan values created by shifting are retained or dropped.

bootstrap_iterationsint, optional, default: None

If not None, compute the p_values of the test, by performing bootstrap.

Examples

>>> from gtime.causality.pearson_correlation import ShiftedPearsonCorrelation
>>> import pandas.util.testing as testing
>>> data = testing.makeTimeDataFrame(freq="s")
>>> spc = ShiftedPearsonCorrelation(target_col="A")
>>> spc.fit(data)
>>> spc.best_shifts_
y  A  B  C  D
x
A  8  9  6  5
B  7  4  4  6
C  3  4  9  9
D  7  1  9  1
>>> spc.max_corrs_
y         A         B         C         D
x
A  0.383800  0.260627  0.343628  0.360151
B  0.311608  0.307203  0.255969  0.298523
C  0.373613  0.267335  0.211913  0.140034
D  0.496535  0.204770  0.402473  0.310065

Methods

fit(self, data)	Create the dataframe of shifts of each time series which maximize the
fit_transform(self, X[, y])	Fit to data, then transform it.
get_params(self[, deep])	Get parameters for this estimator.
set_params(self, \*\*params)	Set the parameters of this estimator.
transform(self, data)	Shifts each input time series by the amount which optimizes correlation with the selected ‘y’ column.

__init__(self, min_shift:int=1, max_shift:int=10, target_col:str='y', dropna:bool=False, bootstrap_iterations:int=None)

Initialize self. See help(type(self)) for accurate signature.

fit(self, data:pandas.core.frame.DataFrame) → 'ShiftedPearsonCorrelation'

Create the dataframe of shifts of each time series which maximize the

Pearson correlation (PPMCC).

Parameters

datapd.DataFrame, shape (n_samples, n_time_series), required

The DataFrame containing the time series on which to compute the shifted correlations.

Returns

selfShiftedPearsonCorrelation

fit_transform(self, X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.

**fit_paramsdict

Additional fit parameters.

Returns

X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfobject

Estimator instance.

transform(self, data:pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Shifts each input time series by the amount which optimizes correlation with the selected ‘y’ column.

Parameters

datapd.DataFrame, shape (n_samples, n_time_series), required

The DataFrame containing the time series on which to perform the transformation.

Returns

data_tpd.DataFrame, shape (n_samples, n_time_series)

The DataFrame (Pivot table) of the shifts which maximize the correlation between each time series. The shift is indicated in rows.