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2024 | OriginalPaper | Chapter

FR\(^3\)LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series

Authors : Abdallah Aaraba, Shengrui Wang, Jean-Marc Patenaude

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer Nature Singapore

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Abstract

While some methods are confined to linear embeddings and others exhibit limited robustness, high-dimensional time series factorization techniques employ scalable matrix factorization for forecasting in latent space. This paper introduces a novel factorization method that employs a non-contrastive approach, guiding an autoencoder-like architecture to extract robust latent series while minimizing redundant information within the embeddings. The resulting learned representations are utilized by a temporal forecasting model, generating forecasts within the latent space, which are subsequently decoded back to the original space through the decoder. Extensive experiments demonstrate that our model achieves state-of-te-art performance on numerous commonly used datasets.

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Metadata
Title
FRLS: A Forecasting Model with Robust and Reduced Redundancy Latent Series
Authors
Abdallah Aaraba
Shengrui Wang
Jean-Marc Patenaude
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-65-5

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

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-2266-2_1

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