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| DeepAR× | Model d'ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Camp≠ | Aprenentatge profund | Econometria |
| Família≠ | Machine learning | Regression model |
| Any d'origen≠ | 2020 | 2015 |
| Autor original≠ | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) | Box & Jenkins (Box-Jenkins methodology) |
| Tipus≠ | Autoregressive recurrent neural network (probabilistic forecasting) | Univariate time-series model |
| Font seminal≠ | 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 ↗ | 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 |
| Àlies | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Relacionats | 5 | 5 |
| Resum≠ | 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. | 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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