Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification

TL;DR

Extreme-SAX (E-SAX) is a novel symbolic representation method for time series that focuses on extreme points, improving classification accuracy while maintaining simplicity and efficiency compared to traditional SAX.

Contribution

E-SAX introduces a new approach that uses only extreme points for time series representation, enhancing classification performance over traditional SAX.

Findings

E-SAX outperforms SAX in classification accuracy across multiple datasets.

E-SAX maintains the simplicity and efficiency of SAX.

Experimental results demonstrate significant improvements in time series classification.

Abstract

Time series classification is an important problem in data mining with several applications in different domains. Because time series data are usually high dimensional, dimensionality reduction techniques have been proposed as an efficient approach to lower their dimensionality. One of the most popular dimensionality reduction techniques of time series data is the Symbolic Aggregate Approximation (SAX), which is inspired by algorithms from text mining and bioinformatics. SAX is simple and efficient because it uses precomputed distances. The disadvantage of SAX is its inability to accurately represent important points in the time series. In this paper we present Extreme-SAX (E-SAX), which uses only the extreme points of each segment to represent the time series. E-SAX has exactly the same simplicity and efficiency of the original SAX, yet it gives better results in time series classification than the original SAX, as we show in extensive experiments on a variety of time series datasets.