方法对比
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| PatchTST× | ARIMA（自回归积分滑动平均）模型× | |
|---|---|---|
| 领域≠ | 深度学习 | 计量经济学 |
| 方法族≠ | Machine learning | Regression model |
| 起源年份≠ | 2023 | 2015 |
| 提出者≠ | Nie, Y. et al. | Box & Jenkins (Box-Jenkins methodology) |
| 类型≠ | Transformer for time series forecasting | Univariate time-series model |
| 开创性文献≠ | Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR. link ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| 别名 | PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| 相关≠ | 3 | 5 |
| 摘要≠ | PatchTST is a patch-based Transformer architecture for time series forecasting, introduced by Nie and colleagues in 2023, that cuts each series into overlapping patches treated as tokens and processes channels independently. It balances computational efficiency with strong accuracy on long-horizon forecasting. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
| ScholarGate数据集 ↗ |
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