Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| TBATS× | Модел ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2011 | 2015 |
| Създател≠ | De Livera, Hyndman & Snyder | Box & Jenkins (Box-Jenkins methodology) |
| Тип≠ | Exponential smoothing state space model | Univariate time-series model |
| Основополагащ източник≠ | De Livera, A. M., Hyndman, R. J. & Snyder, R. D. (2011). Forecasting Time Series with Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), 1513-1527. 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 |
| Други названия≠ | trigonometric exponential smoothing, multiple seasonal exponential smoothing, complex seasonal exponential smoothing, TBATS — Çoklu Mevsimsel Üstel Düzleştirme | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Свързани≠ | 3 | 5 |
| Резюме≠ | TBATS is an innovations state space forecasting model, introduced by De Livera, Hyndman and Snyder (2011), that combines a Box-Cox transformation, ARMA errors and trigonometric (Fourier) seasonal terms. It is built to handle continuous time series with several nested seasonal cycles at once — for example hourly data that also repeats daily, weekly and yearly. | 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). |
| ScholarGateНабор от данни ↗ |
|
|