stats¶

timeseries.stats.mean(values)[source]¶

Return the sample mean of a numeric iterable.

Parameters: values – finite-length iterable of float-convertable numbers
Example

>>> import timeseries as ts

>>> values = (1, 2, 3)
>>> ts.stats.mean(values)
2.0

timeseries.stats.variance(values)[source]¶

Return the unbiased sample variance of a numeric iterable.

Parameters: values – finite-length iterable of float-convertable numbers
Example

>>> import timeseries as ts

>>> values = (1, 2, 3)
>>> ts.stats.variance(values)
1.0

timeseries.stats.crosscovariance(values1, values2)[source]¶

Return the (unnormalized) cross-covariance of two numeric iterables.

Parameters

Example

>>> import timeseries as ts

>>> values1 = (1, 2, 3)
>>> values2 = (-2, -4, -6)
>>> ts.stats.crosscovariance(values1, values2)
-2.0

timeseries.stats.crosscorrelation(values1, values2)[source]¶

Return the (normalized) Pearson cross-correlation of two numeric iterables.

Parameters

Example

>>> import timeseries as ts

>>> values1 = (1, 2, 3)
>>> values2 = (-2, -4, -6)
>>> ts.stats.crosscorrelation(values1, values2)
-1.0

timeseries.stats.adf_test(values, significance=0.05)[source]¶

Return the augmented Dickey-Fuller unit root test statistic and result.

Parameters