Feb. 9, 2024, 5:44 a.m. | Linfeng Du Ji Xin Alex Labach Saba Zuberi Maksims Volkovs Rahul G. Krishnan

cs.LG updates on arXiv.org arxiv.org

Transformer-based models have greatly pushed the boundaries of time series forecasting recently. Existing methods typically encode time series data into $\textit{patches}$ using one or a fixed set of patch lengths. This, however, could result in a lack of ability to capture the variety of intricate temporal dependencies present in real-world multi-periodic time series. In this paper, we propose MultiResFormer, which dynamically models temporal variations by adaptively choosing optimal patch lengths. Concretely, at the beginning of each layer, time series data …

cs.lg data dependencies encode forecasting general modeling series set temporal time series time series forecasting transformer

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