Machine learning
Informer
Informer是Zhou et al.在2021年推出的一种基于Transformer的模型，用于长序列时间序列预测。它采用ProbSparse自注意力机制，将标准Transformer的计算复杂度降低到O(L log L)。该模型专为需要预测未来数千个时间步的问题而设计。
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来源
- Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI. DOI: 10.1609/aaai.v35i12.17325 ↗
- Wu, H., Xu, J., Wang, J. & Long, M. (2021). Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. NeurIPS 34. link ↗
如何引用本页
ScholarGate. (2026, June 1). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. ScholarGate. https://scholargate.app/zh/deep-learning/informer
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