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2024 | OriginalPaper | Buchkapitel

A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification

verfasst von : Zihan Li, Wei Ding, Inal Mashukov, Scott Crouter, Ping Chen

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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Abstract

Time series data plays a significant role in many research fields since it can record and disclose the dynamic trends of a phenomenon with a sequence of ordered data points. Time series data is dynamic, of variable length, and often contains complex patterns, which makes its analysis challenging especially when the amount of data is limited. In this paper, we propose a multi-view feature construction approach that can generate multiple feature sets of different resolutions from a single dataset and produce a fixed-length representation of variable-length time series data. Furthermore, we propose a multi-encoder-decoder Transformer (MEDT) architecture to effectively analyze these multi-view representations. Through extensive experiments using multiple benchmarks and a real-world dataset, our method shows significant improvement over the state-of-the-art methods.

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Vorheriges Kapitel Learning Disentangled Task-Related Representation for Time Series

Nächstes Kapitel Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting

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Titel: A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification
verfasst von: Zihan Li
Wei Ding
Inal Mashukov
Scott Crouter
Ping Chen
Verlag: Springer Nature Singapore
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-981-9722-65-5

Electronic ISBN: 978-981-9722-66-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2266-2_19

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