Comparar métodos
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| Modelo ARIMA (Autoregressive Integrated Moving Average)× | N-HiTS× | |
|---|---|---|
| Campo≠ | Econometría | Aprendizaje profundo |
| Familia≠ | Regression model | Machine learning |
| Año de origen≠ | 2015 | 2023 |
| Autor original≠ | Box & Jenkins (Box-Jenkins methodology) | Challu, C. et al. |
| Tipo≠ | Univariate time-series model | Deep neural forecasting (hierarchical interpolation) |
| Fuente seminal≠ | 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 | 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 | N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | 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). | 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. |
| ScholarGateConjunto de datos ↗ |
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