# TimeSeriesTest¶

class hyppo.time_series.base.TimeSeriesTest(compute_distance=None, max_lag=0, **kwargs)

A base class for a time-series test.

Parameters
• compute_distance (`str`, `callable`, or `None`, default: `"euclidean"`) -- A function that computes the distance among the samples within each data matrix. Valid strings for `compute_distance` are, as defined in `sklearn.metrics.pairwise_distances`,

• From scikit-learn: [`"euclidean"`, `"cityblock"`, `"cosine"`, `"l1"`, `"l2"`, `"manhattan"`] See the documentation for `scipy.spatial.distance` for details on these metrics.

• From scipy.spatial.distance: [`"braycurtis"`, `"canberra"`, `"chebyshev"`, `"correlation"`, `"dice"`, `"hamming"`, `"jaccard"`, `"kulsinski"`, `"mahalanobis"`, `"minkowski"`, `"rogerstanimoto"`, `"russellrao"`, `"seuclidean"`, `"sokalmichener"`, `"sokalsneath"`, `"sqeuclidean"`, `"yule"`] See the documentation for `scipy.spatial.distance` for details on these metrics.

Set to `None` or `"precomputed"` if `x` and `y` are already distance matrices. To call a custom function, either create the distance matrix before-hand or create a function of the form `metric(x, **kwargs)` where `x` is the data matrix for which pairwise distances are calculated and `**kwargs` are extra arguements to send to your custom function.

• max_lag (`float`, default: `0`) -- The maximium lag to consider when computing the test statistics and p-values.

• **kwargs -- Arbitrary keyword arguments for `compute_distance`.

Methods Summary

 Calulates the time-series test statistic. `TimeSeriesTest.test`(x, y[, reps, workers, ...]) Calulates the time-series test test statistic and p-value.

abstract TimeSeriesTest.statistic(x, y)

Calulates the time-series test statistic.

Parameters

x,y (`ndarray` of `float`) -- Input data matrices. `x` and `y` must have the same number of samples. That is, the shapes must be `(n, p)` and `(n, q)` where n is the number of samples and p and q are the number of dimensions. Alternatively, `x` and `y` can be distance matrices, where the shapes must both be `(n, n)`.

abstract TimeSeriesTest.test(x, y, reps=1000, workers=1, random_state=None, is_distsim=False)

Calulates the time-series test test statistic and p-value.

Parameters
• x,y (`ndarray` of `float`) -- Input data matrices. `x` and `y` must have the same number of samples. That is, the shapes must be `(n, p)` and `(n, q)` where n is the number of samples and p and q are the number of dimensions. Alternatively, `x` and `y` can be distance matrices, where the shapes must both be `(n, n)`.

• reps (`int`, default: `1000`) -- The number of replications used to estimate the null distribution when using the permutation test used to calculate the p-value.

• workers (`int`, default: `1`) -- The number of cores to parallelize the p-value computation over. Supply `-1` to use all cores available to the Process.

• is_distsim (`bool`, default: `False`) -- Whether or not `x` and `y` are input matrices.

Returns