Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modeļi ar ilgu atmiņu (ARFIMA, FIGARCH)× | ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis× | |
|---|---|---|
| Nozare≠ | Finanses | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1980 | 2015 |
| Autors≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Box & Jenkins (Box-Jenkins methodology) |
| Tips≠ | Fractionally integrated time series model | Univariate time-series model |
| Pirmavots≠ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. 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 |
| Citi nosaukumi≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. | 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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