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

by   Muhammad Marwan Muhammad Fuad, et al.

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.



page 6

page 8


TSAX is Trending

Time series mining is an important branch of data mining, as time series...

Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information

The Symbolic Aggregate approXimation (SAX) is a very popular symbolic di...

SAX Navigator: Time Series Exploration through Hierarchical Clustering

Comparing many long time series is challenging to do by hand. Clustering...

HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing

Classification of time series data is an important task for many applica...

Experimental Comparison of Representation Methods and Distance Measures for Time Series Data

The previous decade has brought a remarkable increase of the interest in...

High-Dimensional Changepoint Detection via a Geometrically Inspired Mapping

High-dimensional changepoint analysis is a growing area of research and ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.