方法证据记录
Autoformer
Autoformer is a deep learning architecture for long-term time-series forecasting, introduced by Wu et al. from Tsinghua University at NeurIPS 2021. It replaces the standard self-attention mechanism with an Auto-Correlation mechanism that exploits periodic dependencies in the frequency domain, and embeds a progressive series decomposition block throughout the encoder and decoder to separately model trend and seasonal components.
源记录
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Autoformer (Auto-Correlation Decomposition Transformer)
分类方法记录 · ml-model / deep-learning
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