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Multitask Learning for Sparse Failure Prediction Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Discovering All-Chain Set in Streaming Time Series

Authors : Shaopeng Wang, Ye Yuan, Hua Li

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Time series chains discovery is an increasingly popular research area in time series mining. Previous studies on this topic process fixed-length time series. In this work, we focus on the issue of all-chain set mining over the streaming time series, where the all-chain set is a very important kind of the time series chains. We propose a novel all-chain set mining algorithm about streaming time series (ASMSTS) to solve this problem. The main idea behind the ASMSTS is to obtain the mining results at current time-tick based on the ones at the last one. This makes the method more efficiency in time and space than the Naïve. Our experiments illustrate that ASMSTS does indeed detect the all-chain set correctly and can offer dramatic improvements in speed and space cost over the Naive method.

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previous chapter Spatio-Temporal Event Detection from Multiple Data Sources
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Metadata
Title
Discovering All-Chain Set in Streaming Time Series
Authors
Shaopeng Wang
Ye Yuan
Hua Li
Copyright Year
2019
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-16147-7

Electronic ISBN: 978-3-030-16148-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-16148-4_24

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