手法を比較
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| ARIMA（自己回帰和分移動平均）モデル× | PatchTST× | |
|---|---|---|
| 分野≠ | 計量経済学 | 深層学習 |
| 系統≠ | Regression model | Machine learning |
| 提唱年≠ | 2015 | 2023 |
| 提唱者≠ | Box & Jenkins (Box-Jenkins methodology) | Nie, Y. et al. |
| 種類≠ | Univariate time-series model | Transformer for time series forecasting |
| 原典≠ | 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 | Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR. link ↗ |
| 別名 | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer |
| 関連≠ | 5 | 3 |
| 概要≠ | 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). | 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. |
| ScholarGateデータセット ↗ |
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