SiamTST: A Novel Representation Learning Framework for Enhanced Multivariate Time Series Forecasting applied to Telco Networks
<|reference_start|>SiamTST: A Novel Representation Learning Framework for Enhanced Multivariate Time Series Forecasting applied to Telco Networks: 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.<|reference_end|>
@article{kristoffersen2024siamtst:,
    title={SiamTST: A Novel Representation Learning Framework for Enhanced
  Multivariate Time Series Forecasting applied to Telco Networks},
    author={Simen Kristoffersen, Peter Skaar Nordby, Sara Malacarne, Massimiliano
  Ruocco, Pablo Ortiz},
    journal={arXiv preprint arXiv:2407.02258},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02258},
    primaryClass={cs.LG cs.AI}
}