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| Temporal Fusion Transformer× | Modèle ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Domaine≠ | Apprentissage profond | Économétrie |
| Famille≠ | Machine learning | Regression model |
| Année d'origine≠ | 2021 | 2015 |
| Auteur d'origine≠ | Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. | Box & Jenkins (Box-Jenkins methodology) |
| Type≠ | Attention-based deep learning forecasting architecture | Univariate time-series model |
| Source fondatrice≠ | Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. (2021). Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. International Journal of Forecasting, 37(4), 1748–1764. DOI ↗ | 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 |
| Alias | Temporal Fusion Transformer (TFT), TFT, interpretable multi-horizon forecasting transformer | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | The Temporal Fusion Transformer (TFT), introduced by Lim, Arık, Loeff and Pfister in 2021, is an interpretable deep learning architecture for multi-horizon time series forecasting. It combines variable selection, gating, multi-horizon attention and quantile outputs, processing static, past and known-future inputs together to produce multi-step forecasts. | 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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