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| PatchTST× | ARIMA-Modell (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Fachgebiet≠ | Deep Learning | Ökonometrie |
| Familie≠ | Machine learning | Regression model |
| Entstehungsjahr≠ | 2023 | 2015 |
| Urheber≠ | Nie, Y. et al. | Box & Jenkins (Box-Jenkins methodology) |
| Typ≠ | Transformer for time series forecasting | Univariate time-series model |
| Wegweisende Quelle≠ | 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 |
| Aliasnamen | PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | 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). |
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