Transformer
SAXlegacy
Symbolic Aggregate approXimation (SAX) transformer.
as described in Jessica Lin, Eamonn Keogh, Li Wei and Stefano Lonardi, “Experiencing SAX: a novel symbolic representation of time series” Data Mining and Knowledge Discovery, 15(2):107-144 Overview: for each series:
run a sliding window across the series for each window
shorten the series with PAA (Piecewise Approximate Aggregation) discretise the shortened series into fixed bins form a word from these discrete values
by default SAX produces a single word per series (window_size=0). SAX returns a pandas data frame where column 0 is the histogram (sparse pd.series) of each series.
Schnellstart
python
from sktime.transformations.dictionary_based import SAXlegacy
estimator = SAXlegacy(word_length=8, alphabet_size=4, window_size=12, remove_repeat_words=False, save_words=False, return_pandas_data_series=True)Parameter(11)
- word_length: int, length of word to shorten window to (using
- PAA) (default 8)
- alphabet_size: int, number of values to discretise each value
- to (default to 4)
- window_size: int, size of window for sliding. Input series
- length for whole series transform (default to 12)
- remove_repeat_words: boolean, whether to use numerosity reduction (
- default False)
- save_words: boolean, whether to use numerosity reduction (
- default False)
- return_pandas_data_series: boolean, default = True
- set to true to return Pandas Series as a result of transform. setting to true reduces speed significantly but is required for automatic test.