TACDformer: an improved informer-based model for accurate multivariate long-term time series forecasting

Abstract Transformer-based models have exhibited superior performance in the field of multivariate long-sequence time series forecasting. The Informer model, which adopts the probabilistic sparse self-attention mechanism, presents advantages in computational complexity compared to other models. Howe...

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主要な著者:	Zeyu Hu, Yuan Jia, Wu Le, Zhenhong Jia, Congbing He, Huihui Fan, Jie Meng
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2026-04-01
シリーズ:	Scientific Reports
主題:	Multivariate time series Forecasting Transformer-based model Attention mechanism
オンライン･アクセス:	https://doi.org/10.1038/s41598-026-46529-3
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