SAX, HOTSAX, EMMA implementations for Python
Project description
- 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]
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)
Patel, P., Keogh, E., Lin, J., Lonardi, S., Mining Motifs in Massive Time Series Databases, In Proc. ICDM (2002)
Keogh, E., Lin, J., Fu, A., HOT SAX: Efficiently finding the most unusual time series subsequence, In Proc. ICDM (2005)
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:
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.
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
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