pyhctsa.operations.symbolic.motif_two¶

pyhctsa.operations.symbolic.motif_two(y, binarize_how='diff')¶

Compute local motifs in a binary symbolization of the input time series.

This function coarse-grains the input time series into a binary sequence using the specified binarization method, and computes the probabilities of binary words of lengths 1 through 4, along with their entropies.

Parameters:¶

y : array-like¶

The input time series.

binarize_how : str, optional¶

The method used for binary transformation. One of:

• ’diff’: Encode increases in the time series as 1, and decreases as 0 (default).

• ’mean’: Encode values above the mean as 1, and below as 0.

• ’median’: Encode values above the median as 1, and below as 0.

Returns:¶

A dictionary containing:

• ’prob_len_1’, ‘prob_len_2’, …, ‘prob_len_4’:

  Lists of probabilities for each binary word of lengths 1 to 4.

• ’entropy_len_1’, ‘entropy_len_2’, …, ‘entropy_len_4’:

  Entropy values associated with the word distributions of lengths 1 to 4.

Return type:¶

dict