TSAX is Trending

TL;DR

This paper introduces Trending SAX (TSAX), a simple yet effective modification of SAX that enhances trend representation in time series classification, demonstrating superior accuracy on multiple datasets.

Contribution

The paper proposes TSAX, a minimal-complexity extension of SAX, which significantly improves trend capturing and classification performance in time series analysis.

Findings

TSAX outperforms SAX on 39 out of 50 datasets.

TSAX achieves lower classification error rates.

The method maintains simplicity with minimal added complexity.

Abstract

Time series mining is an important branch of data mining, as time series data is ubiquitous and has many applications in several domains. The main task in time series mining is classification. Time series representation methods play an important role in time series classification and other time series mining tasks. One of the most popular representation methods of time series data is the Symbolic Aggregate approXimation (SAX). The secret behind its popularity is its simplicity and efficiency. SAX has however one major drawback, which is its inability to represent trend information. Several methods have been proposed to enable SAX to capture trend information, but this comes at the expense of complex processing, preprocessing, or post-processing procedures. In this paper we present a new modification of SAX that we call Trending SAX (TSAX), which only adds minimal complexity to SAX, but substantially improves its performance in time series classification. This is validated experimentally on 50 datasets. The results show the superior performance of our method, as it gives a smaller classification error on 39 datasets compared with SAX.