pyhctsa.operations.nonlinearity.embed_pca¶

pyhctsa.operations.nonlinearity.embed_pca(y, tau='ac', m=3)¶

Reconstructs the time series as a time-delay embedding, and performs Principal Components Analysis on the result. This technique is known as singular spectrum analysis [1].

References

Parameters:¶

y : array-like¶

Input time series.

tau : str or int¶

The time-delay: an integer, or a rule understood by pyhctsa.utils.get_tau(). 'ac' is the first zero-crossing of the autocorrelation function, 'ac1e' the (floored) first 1/e crossing of the autocorrelation function, and 'mi' the smaller of the first minimum of the (Kraskov) automutual information and the 1/e time. Default is 'ac'.

m : int¶

The embedding dimension. Default is 3.

Returns:¶

Various statistics summarizing the obtained eigenvalue distribution.

Return type:¶

dict