方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| FreTS：用于时间序列预测的频域MLP× | TSMixer：全MLP架构用于时间序列预测× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份 | 2023 | 2023 |
| 提出者≠ | Kun Yi et al. | Si-An Chen et al. (Google) |
| 类型≠ | Frequency-domain MLP forecasting model | All-MLP multivariate time-series forecasting model |
| 开创性文献≠ | Yi, K., Zhang, Q., Fan, W., Wang, S., Wang, P., He, H., An, N., Lian, D., Cao, L., & Niu, Z. (2023). Frequency-domain MLPs are more effective learners in time series forecasting. NeurIPS. link ↗ | Chen, S.-A., Li, C.-L., Yoder, N., Arik, S. O., & Pfister, T. (2023). TSMixer: An all-MLP architecture for time series forecasting. Transactions on Machine Learning Research. link ↗ |
| 别名 | Frequency-domain MLPs, FrequencyMLP, FreTS Forecaster, Frekans Alanı MLP | All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı |
| 相关 | 3 | 3 |
| 摘要≠ | FreTS is a time series forecasting architecture introduced by Yi et al. at NeurIPS 2023. It departs from Transformer-based designs by applying simple Multi-Layer Perceptrons (MLPs) entirely in the frequency domain. The model transforms input sequences with the Discrete Fourier Transform and then learns temporal and channel dependencies through complex-valued MLP layers, achieving competitive or superior long-term forecasting accuracy with substantially lower computational cost. | TSMixer is a multivariate time-series forecasting model introduced by Si-An Chen and colleagues at Google in 2023. It challenges the prevailing dominance of Transformer-based architectures by demonstrating that a simple stack of interleaved MLP layers — alternating between mixing along the time axis and mixing across feature channels — achieves strong forecasting accuracy while remaining computationally efficient and easy to interpret architecturally. |
| ScholarGate数据集 ↗ |
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