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| DeepAR× | Konform prediktion för tidsserieprognoser× | |
|---|---|---|
| Ämnesområde≠ | Djupinlärning | Ekonometri |
| Familj≠ | Machine learning | Regression model |
| Ursprungsår≠ | 2020 | 2021 |
| Upphovsperson≠ | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) |
| Typ≠ | Autoregressive recurrent neural network (probabilistic forecasting) | Distribution-free prediction interval wrapper |
| Ursprungskälla≠ | 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 ↗ | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ |
| Alias≠ | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) |
| Närliggande≠ | 5 | 4 |
| Sammanfattning≠ | 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. | Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023). |
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