`gtime.causality`.ShiftedLinearCoefficient¶

class gtime.causality.ShiftedLinearCoefficient(min_shift: int = 1, max_shift: int = 10, target_col: str = None, dropna: bool = False, bootstrap_iterations: int = None, permutation_iterations: int = None)¶

Test the shifted linear fit coefficients between two or more time series.

Parameters:

min_shift : int, optional, default: 1: The minimum number of shifts to check for.
max_shift : int, optional, default: 10: The maximum number of shifts to check for.
target_col : str, optional, default: None: The column to use as the a reference (i.e., the column which is not shifted).
dropna : bool, optional, default: False: Determines if the Nan values created by shifting are retained or dropped.
bootstrap_iterations : int, optional, default: None: If not None, compute the p_values of the test, by performing bootstrapping of the original data (sampling with replacement).
permutation_iterations : int, optional, default: None: If not None, compute the p_values of the test, by performing permutations of the original data.

Examples

>>> from gtime.causality.linear_coefficient import ShiftedLinearCoefficient
>>> import pandas.util.testing as testing
>>> data = testing.makeTimeDataFrame(freq="s")
>>> slc = ShiftedLinearCoefficient(target_col="A")
>>> slc.fit(data)
>>> slc.best_shifts_
y  A  B  C  D
x
A  3  6  8  5
B  9  9  4  1
C  8  2  4  9
D  3  9  4  3
>>> slc.max_corrs_
y         A         B         C         D
x
A  0.460236  0.420005  0.339370  0.267143
B  0.177856  0.300350  0.367150  0.550490
C  0.484860  0.263036  0.456046  0.251342
D  0.580068  0.344688  0.253626  0.256220

Methods

`fit`(self, data)	Create the DataFrame of shifts of each time series which maximize the shifted
`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 ‘target_col’ column.

__init__(self, min_shift: int = 1, max_shift: int = 10, target_col: str = None, dropna: bool = False, bootstrap_iterations: int = None, permutation_iterations: int = None)¶: Initialize self. See help(type(self)) for accurate signature.

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

Create the DataFrame of shifts of each time series which maximize the shifted: linear fit coefficients.

Parameters:	data : pd.DataFrame, shape (n_samples, n_time_series), required The DataFrame containing the time-series on which to compute the shifted linear fit coefficients.
Returns:	self : `ShiftedLinearCoefficient`

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:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values. **fit_params : dict Additional fit parameters.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(self, deep=True)¶

Get parameters for this estimator.

Parameters:	deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping 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:	**params : dict Estimator parameters.
Returns:	self : object 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 ‘target_col’ column. If no target column is specified, the first column of the DataFrame is taken as the target.

Parameters:	data : pd.DataFrame, shape (n_samples, n_time_series), required The DataFrame containing the time series on which to perform the transformation.
Returns:	data_t : pd.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.

gtime.causality.ShiftedLinearCoefficient¶

`gtime.causality`.ShiftedLinearCoefficient¶