autoformer

Autoformer是一种基于Transformer结构的时序预测模型，它在Self-attention的改进上进行了创新。与之前的模型不同，Autoformer更关注将Self-attention应用于时序计算，并设计了更适合时序分析的attention结构。\[1\]\[2\] Autoformer是对原始Transformer进行了一系列优化，包括Series Decomposition Block模块和对多头注意力机制的升级AutoCorrelationMechanism。如果你对Autoformer的细节感兴趣，可以参考杰少的文章，其中详细介绍了Autoformer的各个模块。\[3\]
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- *1* *2* [Autoformer算法与代码分析](https://blog.csdn.net/buchuankuzi_/article/details/128390246)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *3* [Transformer在时序预测的应⽤第一弹——Autoformer](https://blog.csdn.net/m0_46413065/article/details/129658692)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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