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

Autor:	Kristoffersen, Simen, Nordby, Peter Skaar, Malacarne, Sara, Ruocco, Massimiliano, Ortiz, Pablo
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 14 pages, 3 figures, public codebase
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.02258 Zobrazit plný text záznamu View this record from Arxiv