Machine learningTime-series forecasting
SCINet：用于时间序列预测的样本卷积与交互网络
SCINet 是由 Liu 等人于 NeurIPS 2022 提出的一个用于多步时间序列预测的深度学习架构。其核心思想是采用一种递归二叉树结构的 SCI-Blocks，每个块将输入序列分割为奇数和偶数索引的子序列，应用卷积滤波器对跨子序列的交互进行建模，然后合并学习到的表示。这种分层的下采样策略使网络能够同时捕捉多个分辨率下的时间依赖性。
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来源
- Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., & Xu, Q. (2022). SCINet: Time series modeling and forecasting with sample convolution and interaction. NeurIPS. link ↗
如何引用本页
ScholarGate. (2026, June 2). SCINet (Sample Convolution and Interaction Network). ScholarGate. https://scholargate.app/zh/deep-learning/scinet
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