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Analyzing the Research Landscape of DaWaK Papers from 1999 to 2019 Read chapter Read first chapter

2020 | OriginalPaper | Chapter

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

Author : Muhammad Marwan Muhammad Fuad

Published in: Big Data Analytics and Knowledge Discovery

Publisher: Springer International Publishing

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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.

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Metadata
Title
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification
Author
Muhammad Marwan Muhammad Fuad
Copyright Year
2020
Book
Big Data Analytics and Knowledge Discovery

Print ISBN: 978-3-030-59064-2

Electronic ISBN: 978-3-030-59065-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-59065-9_10

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