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Symbolic Aggregate approXimation, HOT-SAX, and SAX-VSM implementation in Python

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Time series symbolic discretization with SAX

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This code is released under GPL v.2.0 and implements in Python: * Symbolic Aggregate approXimation (SAX) (with z-normalization and PAA) [1] * EMMA -- an algorithm for time series motif discovery [2] * HOT-SAX - a time series anomaly (discord) discovery algorithm [3] * SAX-ZSCORE - an extension to SAX for multi-dimensional time-series SAX that modifies the z-normalization to multi-dimensional sequences and PAA aggregation with the average along data-dimensions. [4] * SAX-REPEAT - an extension to SAX for multi-dimensional time-series that performs standard SAX on individual dimensions, then clusters to map multi-dimensional words into strings from the required alphabet size. [4]

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


[1] 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)

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

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

[4] Mohammad, Y., Nishida T.,
Robust learning from demonstrations using multidimensional SAX, 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)

Citing this work

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

[Citation] 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 Conference, 2014.

SAX 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). 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.


The code is written in Python and hosted on PyPi, so use

to install it. This is what happens in my clean test environment:
$ pip install saxpy
Collecting saxpy
Downloading saxpy-1.0.0.dev154.tar.gz (180kB)
    100% |████████████████████████████████| 184kB 778kB/s 
Requirement already satisfied: numpy in /home/psenin/anaconda3/lib/python3.6/site-packages (from saxpy)
Requirement already satisfied: pytest in /home/psenin/anaconda3/lib/python3.6/site-packages (from saxpy)
Installing collected packages: coverage, pytest-cov, codecov, saxpy
Successfully installed codecov-2.0.15 coverage-4.5.1 pytest-cov-2.5.1 saxpy-1.0.0.dev154

Simple time series to SAX conversion

To convert a time series of an arbitrary length to SAX we need to define the alphabet cuts. Saxpy retrieves cuts for a normal alphabet (we use size 3 here) via

from saxpy.alphabet import cuts_for_asize

which yields an array:

array([      -inf, -0.4307273,  0.4307273])

To convert a time series to letters with SAX we use

function but not forgetting to z-normalize the input time series (we use Normal alphabet):
import numpy as np
from saxpy.znorm import znorm
from saxpy.sax import ts_to_string
ts_to_string(znorm(np.array([-2, 0, 2, 0, -1])), cuts_for_asize(3))

this produces a string:


Time series to SAX conversion with PAA aggregation (by "chunking")

In order to reduce dimensionality further, the PAA (Piecewise Aggregate Approximation) is usually applied prior to SAX:

import numpy as np
from saxpy.znorm import znorm
from saxpy.paa import paa
from saxpy.sax import ts_to_string

dat = np.array([-2, 0, 2, 0, -1]) dat_znorm = znorm(dat) dat_paa_3 = paa(dat_znorm, 3)

ts_to_string(dat_paa_3, cuts_for_asize(3))

and a string with three letters is produced:


Time series to SAX conversion via sliding window

Typically, in order to investigate the input time series structure in order to discover anomalous (i.e., discords) and recurrent (i.e., motifs) patterns we employ time series to SAX conversion via sliding window. Saxpy implements this workflow:

import numpy as np
from saxpy.sax import sax_via_window

dat = np.array([0., 0., 0., 0., 0., -0.270340178359072, -0.367828308500142, 0.666980581124872, 1.87088147328446, 2.14548907684624, -0.480859313143032, -0.72911654245842, -0.490308602315934, -0.66152028906509, -0.221049033806403, 0.367003418871239, 0.631073992586373, 0.0487728723414486, 0.762655178750436, 0.78574757843331, 0.338239686422963, 0.784206454089066, -2.14265084073625, 2.11325193044223, 0.186018356196443, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.519132472499234, -2.604783141655, -0.244519550114012, -1.6570790528784, 3.34184602886343, 2.10361226260999, 1.9796808733979, -0.822247322003058, 1.06850578033292, -0.678811824405992, 0.804225748913681, 0.57363964388698, 0.437113583759113, 0.437208643628268, 0.989892093383503, 1.76545983424176, 0.119483882364649, -0.222311941138971, -0.74669456611669, -0.0663660879732063, 0., 0., 0., 0., 0.,])

sax_none = sax_via_window(dat, win_size=6, paa_size=3, alphabet_size=3, nr_strategy=None, znorm_threshold=0.01)


the result is represented as a data structure of resulting words and their respective positions on time series:

        {'aac': [4, 10, 11, 30, 35],
         'abc': [12, 14, 36, 44],
         'acb': [5, 16, 21, 37, 43],
         'acc': [13, 52, 53],
         'bac': [3, 19, 34, 45, 51],
         'bba': [31],
         'bbb': [15, 18, 20, 22, 25, 26, 27, 28, 29, 41, 42, 46],
         'bbc': [2],
         'bca': [6, 17, 32, 38, 47, 48],
         'caa': [8, 23, 24, 40],
         'cab': [9, 50],
         'cba': [7, 39, 49],
         'cbb': [33],
         'cca': [0, 1]})

is parameterised with a sliding window size, desired PAA aggregation, alphabet size, a numerosity reduction strategy, z-normalization threshold, and a SAX type ('unidim' for unidimensional SAX (default), 'zscore' for SAX-ZSCORE, 'repeat' for SAX-REPEAT):
def sax_via_window(series, win_size, paa_size, alphabet_size=3,
               nr_strategy='exact', z_threshold=0.01, sax_type='unidim')

Time series discord discovery with HOT-SAX

Saxpy implements HOT-SAX discord discovery algorithm in

function which can be used as follows:
import numpy as np
from saxpy.hotsax import find_discords_hotsax
from numpy import genfromtxt
dd = genfromtxt("data/ecg0606_1.csv", delimiter=',')  
discords = find_discords_hotsax(dd)

and discovers anomalies easily:

[(430, 5.2790800061718386), (318, 4.1757563573086953)]

The function has a similar parameterization: sliding window size, PAA and alphabet sizes, z-normalization threshold, and a parameter specifying how many discords are desired to be found:

def find_discords_hotsax(series, win_size=100, num_discords=2, a_size=3,
                     paa_size=3, z_threshold=0.01)

Saxpy also provides a brute-force implementation of the discord search if you'd like to verify discords or evaluate the speed-up:

find_discords_brute_force(series, win_size, num_discords=2,

which can be called as follows:

discords = find_discords_brute_force(dd[100:500], 100, 4)

[(73, 6.198555329625453), (219, 5.5636923991016136)]

Time series motif discovery with EMMA


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