README.rst

Time series symbolic discretization with SAX

This code is released under GPL v.2.0 and implements in Python:

• Symbolic Aggregate approXimation (i.e., SAX) stack [LIN2002]
• a simple function for time series motif discovery [PATEL2001]
• HOT-SAX - a time series anomaly (discord) discovery algorithm [KEOGH2005]

Note that the most of the library's functionality is also available in R and Java

Citing this work:

If you are using this implementation for you academic work, please cite our Grammarviz 2.0 paper:

In a nutshell

SAX is used to transform a sequence of rational numbers (i.e., a time series) into a sequence of letters (i.e., a string) which is (typically) much shorterthan the input time series. Thus, SAX transform addresses a chief problem in time-series analysis -- the dimensionality curse.

This is an illustration of a time series of 128 points converted into the word of 8 letters:

SAX in a nutshell

As discretization is probably the most used transformation in data mining, SAX has been widely used throughout the field. Find more information about SAX at its authors pages: SAX overview by Jessica Lin, Eamonn Keogh's SAX page, or at sax-vsm wiki page.

Installation

$ pip install saxpy

Requirements

Compatibility

Licence

GNU General Public License v2.0

Authors

saxpy was written by Pavel Senin.

KEOGH2005

Keogh, E., Lin, J., Fu, A., HOT SAX: Efficiently finding the most unusual time series subsequence, In Proc. ICDM (2005)

LIN2002

Lin, J., Keogh, E., Patel, P., and Lonardi, S., Finding Motifs in Time Series, The 2nd Workshop on Temporal Data Mining, the 8th ACM Int'l Conference on KDD (2002)

PATEL2001

Patel, P., Keogh, E., Lin, J., Lonardi, S., Mining Motifs in Massive Time Series Databases, In Proc. ICDM (2002)

SENIN2014

Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S., Lerner, M., GrammarViz 2.0: a tool for grammar-based pattern discovery in time series, ECML/PKDD, 2014.