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| Модел ARIMA (Autoregressive Integrated Moving Average)× | Анализ на прекъснати времеви редове (ITS)× | |
|---|---|---|
| Област≠ | Иконометрия | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2015 | 2002 |
| Създател≠ | Box & Jenkins (Box-Jenkins methodology) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Тип≠ | Univariate time-series model | Quasi-experimental segmented regression |
| Основополагащ източник≠ | 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 | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ |
| Други названия | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Свързани | 5 | 5 |
| Резюме≠ | 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). | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. |
| ScholarGateНабор от данни ↗ |
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