方法对比
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| SCINet：用于时间序列预测的样本卷积与交互网络× | TimesNet：面向时间序列的二维时变建模× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2022 | 2023 |
| 提出者≠ | Minhao Liu et al. | Haixu Wu et al. |
| 类型≠ | Hierarchical convolutional time-series forecasting network | 2D convolutional time-series model |
| 开创性文献≠ | 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 ↗ | Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., & Long, M. (2023). TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR. link ↗ |
| 别名 | Sample Convolution and Interaction Network, SCI-Net, Temporal Downsampling Convolution Network, Örneklem Evrişim ve Etkileşim Ağı | Temporal 2D-Variation Network, TimesNet Model, 2D Time-Series Network, Zamansal 2B Varyasyon Ağı |
| 相关 | 2 | 2 |
| 摘要≠ | SCINet is a deep learning architecture for multi-step time-series forecasting introduced by Liu et al. at NeurIPS 2022. Its core idea is a recursive binary-tree structure of SCI-Blocks, each of which splits an input sequence into odd- and even-indexed sub-sequences, applies convolutional filters to model cross-subsequence interactions, and then merges the learned representations. This hierarchical downsampling strategy enables the network to capture temporal dependencies at multiple resolutions simultaneously. | TimesNet is a general-purpose time-series model introduced by Wu et al. at ICLR 2023. Its central idea is that univariate or multivariate time series can be reinterpreted as collections of two-dimensional temporal maps by reshaping the 1D signal according to its dominant periodicities, detected via Fast Fourier Transform. This 1D-to-2D transformation exposes both intraperiod patterns (within one cycle) and interperiod trends (across cycles), enabling powerful 2D convolutional architectures to model temporal variation. |
| ScholarGate数据集 ↗ |
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