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SiamTST: A Novel Representation Learning Framework for Enhanced Multivariate Time Series Forecasting applied to Telco Networks

arXiv 2407.02258 ↗First posted 2 Jul 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

We introduce SiamTST, a novel representation learning framework for multivariate time series. SiamTST integrates a Siamese network with attention, channel-independent patching, and normalization techniques to achieve superior performance. Evaluated on a real-world industrial telecommunication dataset, SiamTST demonstrates significant improvements in forecasting accuracy over existing methods. Notably, a simple linear network also shows competitive performance, achieving the second-best results, just behind SiamTST. The code is available at https://github.com/simenkristoff/SiamTST.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 2 Jul 2024.

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References · 16

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4 more
  1. 2020Query-Key Normalization for Transformerscited by 280
  2. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 269
  3. 2022CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecastingcited by 144
  4. 2023Simple Contrastive Representation Learning for Time Series Forecastingcited by 7

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