sax-ts 1.1

Symbolic Aggregate approXimation (SAX)

Symbolic Aggregate approXimation

This package implements the Symbolic Aggregate approXimation (SAX) algorithm in C++ with Python bindings using pybind11. SAX is a time series discretization method that represents a continuous-valued time series as a (sliding window of) symbolic representation(s).

Jessica Lin's SAX page

Eamonn Keogh's SAX page

Installation

The easiest way to install sax-ts is via pip:

pip install sax-ts

If you want to run the tests locally, install the optional dependencies:

pip install "sax-ts[test]"

You can then import the sax and paa functions as follows:

from sax_ts import sax, paa

References

P. Patel, E. Keogh, J. Lin and S. Lonardi, "Mining motifs in massive time series databases," 2002 IEEE International Conference on Data Mining, 2002. Proceedings., Maebashi City, Japan, 2002, pp. 370-377, doi: 10.1109/ICDM.2002.1183925.

J. Lin, E. Keogh, S. Lonardi, and B. Chiu, "A symbolic representation of time series, with implications for streaming algorithms," In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, 2003, pp. 2-11, doi: 10.1145/882082.882086.

J. Lin, E. Keogh, L. Wei, and S. Lonardi, "Experiencing SAX: a novel symbolic representation of time series," Data Min Knowl Disc, vol. 15, pp. 107–144, Apr. 2007, doi: 10.1007/s10618-007-0064-z.