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Communication Dans Un Congrès Année : 2023

Rankformer: Leveraging Rank Correlation for Transformer-based Time Series Forecasting

Résumé

Long-term forecasting problem for time series has been actively studied during the last several years, and preceding Transformer-based models have exploited various self-attention mechanisms to discover the long-range dependencies. However, the hidden dependencies required by the forecasting task are not always appropriately extracted, especially the nonlinear serial dependencies in some datasets. In this paper, we propose a novel Transformer-based model, namely Rankformer, leveraging the rank correlation function and decomposition architecture for long-term time series forecasting tasks. Rankformer outperforms four state-of-the-art Transformer-based models and two RNNbased models for different forecasting horizons on different datasets on which extensive experiments were conducted.
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Dates et versions

hal-04110209 , version 1 (30-05-2023)

Identifiants

  • HAL Id : hal-04110209 , version 1

Citer

Zuokun Ouyang, Meryem Jabloun, Philippe Ravier. Rankformer: Leveraging Rank Correlation for Transformer-based Time Series Forecasting. IEEE Statistical Signal Processing Workshop, Jul 2023, Hanoi, Vietnam. ⟨hal-04110209⟩
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