# TSAX is Trending

arXiv: https://arxiv.org/abs/2112.12912 · HTML: https://mlsanity.com/paper/2112.12912/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Adversarial Attacks on Deep Neural Networks for Time Series Classification](https://mlsanity.com/paper/1903.07054/) (2019)
- [Insights into LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1902.10756/) (2019)
- [Bag of Recurrence Patterns Representation for Time-Series Classification](https://mlsanity.com/paper/1803.11111/) (2018)
- [Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information](https://mlsanity.com/paper/2010.00730/) (2020)
- [Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification](https://mlsanity.com/paper/2010.00732/) (2020)

## Abstract (arXiv)

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.