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| Model ARIMA (Autoregressive Integrated Moving Average)× | Konformná predikcia pre časové rady× | N-HiTS× | |
|---|---|---|---|
| Odbor≠ | Ekonometria | Ekonometria | Hlboké učenie |
| Rodina≠ | Regression model | Regression model | Machine learning |
| Rok vzniku≠ | 2015 | 2021 | 2023 |
| Tvorca≠ | Box & Jenkins (Box-Jenkins methodology) | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) | Challu, C. et al. |
| Typ≠ | Univariate time-series model | Distribution-free prediction interval wrapper | Deep neural forecasting (hierarchical interpolation) |
| Pôvodný zdroj≠ | 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 | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ | Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI. DOI ↗ |
| Ďalšie názvy≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) | N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation |
| Príbuzné≠ | 5 | 4 | 3 |
| Zhrnutie≠ | 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). | 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). | 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. |
| ScholarGateDátová sada ↗ |
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