SFA

Symbolic Fourier Approximation (SFA) Transformer.

Quickstart

from sktime.transformations.dictionary_based import SFA

estimator = SFA(word_length=8, alphabet_size=4, window_size=12, norm=False, binning_method='equi-depth', anova=False, bigrams=False, skip_grams=False, remove_repeat_words=False, levels=1, lower_bounding=True, save_words=False, keep_binning_dft=False, return_pandas_data_series=False, use_fallback_dft=False, typed_dict=False, n_jobs=1)

Parameters(13)

word_length: int, default = 8

length of word to shorten window to (using PAA)

alphabet_size: int, default = 4

number of values to discretise each value to

window_size: int, default = 12

size of window for sliding. Input series length for whole series transform

norm: boolean, default = False

mean normalise words by dropping first fourier coefficient

binning_method: {“equi-depth”, “equi-width”, “information-gain”, “kmeans”},

default=”equi-depth”

the binning method used to derive the breakpoints.

anova: boolean, default = False

If True, the Fourier coefficient selection is done via a one-way ANOVA test. If False, the first Fourier coefficients are selected. Only applicable if labels are given

bigrams: boolean, default = False

whether to create bigrams of SFA words

skip_grams: boolean, default = False

whether to create skip-grams of SFA words

remove_repeat_words: boolean, default = False

whether to use numerosity reduction (default False)

levels: int, default = 1

Number of spatial pyramid levels

save_words: boolean, default = False

whether to save the words generated for each series (default False)

return_pandas_data_series: boolean, default = False

set to true to return Pandas Series as a result of transform. setting to true reduces speed significantly but is required for automatic test.

n_jobs: int, optional, default = 1

The number of jobs to run in parallel for both transform. -1 means using all processors.

References

1. [1 ] Schäfer, Patrick, and Mikael Högqvist. “SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets.” Proceedings of the 15th international conference on extending database technology. 2012.