Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis× | Lokālā regresija LOESS / LOWESS× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Mašīnmācīšanās |
| Saime≠ | Regression model | Machine learning |
| Izcelsmes gads≠ | 2015 | 1979 |
| Autors≠ | Box & Jenkins (Box-Jenkins methodology) | William S. Cleveland |
| Tips≠ | Univariate time-series model | Local nonparametric regression smoother |
| Pirmavots≠ | 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 | Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI ↗ |
| Citi nosaukumi≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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). | LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots. |
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