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The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Signal2Vec: Time Series Embedding Representation

Authors : Christoforos Nalmpantis, Dimitris Vrakas

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

The rise of Internet-of-Things (IoT) and the exponential increase of devices using sensors, has lead to an increasing interest in data mining of time series. In this context, several representation methods have been proposed. Signal2vec is a novel framework, which can represent any time-series in a vector space. It is unsupervised, computationally efficient, scalable and generic. The framework is evaluated via a theoretical analysis and real world applications, with a focus on energy data. The experimental results are compared against a baseline using raw data and two other popular representations, SAX and PAA. Signal2vec is superior not only in terms of performance, but also in efficiency, due to dimensionality reduction.

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Metadata
Title
Signal2Vec: Time Series Embedding Representation
Authors
Christoforos Nalmpantis
Dimitris Vrakas
Copyright Year
2019
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-030-20256-9

Electronic ISBN: 978-3-030-20257-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-20257-6_7

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