Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Temporal Fusion Transformer× | Модел ARIMA (Autoregressive Integrated Moving Average)× | DeepAR× | |
|---|---|---|---|
| Област≠ | Дълбоко обучение | Иконометрия | Дълбоко обучение |
| Семейство≠ | Machine learning | Regression model | Machine learning |
| Година на възникване≠ | 2021 | 2015 | 2020 |
| Създател≠ | Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. | Box & Jenkins (Box-Jenkins methodology) | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) |
| Тип≠ | Attention-based deep learning forecasting architecture | Univariate time-series model | Autoregressive recurrent neural network (probabilistic forecasting) |
| Основополагащ източник≠ | 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 | Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191. DOI ↗ |
| Други названия | Temporal Fusion Transformer (TFT), TFT, interpretable multi-horizon forecasting transformer | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR |
| Свързани≠ | 6 | 5 | 5 |
| Резюме≠ | 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). | DeepAR is Amazon's industrial forecasting model, introduced by Salinas, Flunkert and Gasthaus (2017; published 2020), that uses an autoregressive recurrent neural network to estimate the parameters of a probability distribution at each step, producing a confidence interval rather than a single point forecast. It can model many related time series jointly within one model. |
| ScholarGateНабор от данни ↗ |
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