Feb. 15, 2024, 5:42 a.m. | Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09373v1 Announce Type: new
Abstract: Several applications in time series forecasting require predicting multiple steps ahead. Despite the vast amount of literature in the topic, both classical and recent deep learning based approaches have mostly focused on minimising performance averaged over the predicted window. We observe that this can lead to disparate distributions of errors across forecasting steps, especially for recent transformer architectures trained on popular forecasting benchmarks. That is, optimising performance on average can lead to undesirably large errors …

abstract applications arxiv constraints cs.lg deep learning forecasting literature long-term loss multiple observe performance series stat.ml time series time series forecasting type vast

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