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| Modello ARIMA (Autoregressive Integrated Moving Average)× | DeepAR× | N-HiTS× | |
|---|---|---|---|
| Campo≠ | Econometria | Apprendimento profondo | Apprendimento profondo |
| Famiglia≠ | Regression model | Machine learning | Machine learning |
| Anno di origine≠ | 2015 | 2020 | 2023 |
| Ideatore≠ | Box & Jenkins (Box-Jenkins methodology) | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) | Challu, C. et al. |
| Tipo≠ | Univariate time-series model | Autoregressive recurrent neural network (probabilistic forecasting) | Deep neural forecasting (hierarchical interpolation) |
| Fonte seminale≠ | 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 ↗ | Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI. DOI ↗ |
| Alias | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR | N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation |
| Correlati≠ | 5 | 5 | 3 |
| Sintesi≠ | 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. | N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting), introduced by Challu and colleagues in 2023, is a deep neural forecasting architecture that combines the hierarchical forecasts of multiple stacks operating at different sampling rates and merges them through interpolation. It extends N-BEATS to deliver markedly better accuracy on long forecast horizons. |
| ScholarGateInsieme di dati ↗ |
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