This page describes the usage of the SAX-SEQL software and supports our publication:
Time Series Classification by Sequence Learning in All-Subsequence Space, ICDE 2017 IEEE International Conference on Data Engineering, San Diego, Thach Le Nguyen, Severin Gsponer, Georgiana Ifrim (Insight Centre for Data Analytics - University College Dublin)
SAX is a transformation method to convert numeric vector to a symbolic representation, i.e. a sequence of symbols from a predefined alphabet a. SAX first computes the Piecewise Aggregate Approximation (PAA) of a time series and then transforms this approximation to a symbolic representation.
PAA reduces a time series of length L to a vector of length l (l < L is also the length of the symbolic sequence) by dividing the time series into equal segments. Each segment is then replaced with its mean value.
PAA followed by a discretisation step which replaces each value of the PAA with a corresponding symbol. Symbol is selected from the alphabet based on the interval in which the value falls. There are a intervals, as many as the size of the alphabet. Each interval is associated with a symbol from the alphabet. Assuming that the time series is normal distributed, the intervals are divided under the normal distribution (i.e. N(0,1)) with equal probability.
The original SEQL software and its description can be found here: https://github.com/heerme/seql-sequence-learner
To compile execute following commands in the src directory:
mkdir -p build
cd build
mkdir -p Release
cd Release
cmake -DCMAKE_BUILD_TYPE=Release ../../
make
./sax_seql -t data/Coffee_TRAIN -T data/Coffee_TEST -d [directory for output] -n 60 -w 16 -a 4