DBAFormer: A Double-Branch Attention Transformer for Long-Term Time Series Forecasting

DBAFormer: A Double-Branch Attention Transformer for Long-Term Time Series Forecasting

Abstract The transformer-based approach excels in long-term series forecasting. These models leverage stacking structures and self-attention mechanisms, enabling them to effectively model dependencies in series data. While some approaches prioritize sparse attention to tackle the quadratic time comp...

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Bibliographic Details
Main Authors: Ji Huang, Minbo Ma, Yongsheng Dai, Jie Hu, Shengdong Du
Format: Article
Language:English
Published: Springer Nature 2023-07-01
Series:Human-Centric Intelligent Systems
Subjects:
Time series forecasting
Self-attention
Encoder-decoder
Transformer-based models
Online Access:https://doi.org/10.1007/s44230-023-00037-z

Internet

https://doi.org/10.1007/s44230-023-00037-z

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