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| ARIMA (Autoregressive Integrated Moving Average) Model× | SARIMA (Seasonal ARIMA)× | STL-dekomponering: Sæson-trend-dekomponering ved hjælp af Loess× | |
|---|---|---|---|
| Fagområde | Økonometri | Økonometri | Økonometri |
| Familie≠ | Regression model | Regression model | Process / pipeline |
| Oprindelsesår≠ | 2015 | 2015 | 1990 |
| Ophavsperson≠ | Box & Jenkins (Box-Jenkins methodology) | Box & Jenkins (seasonal extension of ARIMA) | Cleveland, Cleveland, McRae & Terpenning |
| Type≠ | Univariate time-series model | Seasonal time-series model | nonparametric iterative smoother |
| Oprindelig kilde≠ | 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 | 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, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73. link ↗ |
| Aliasser≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) |
| Relaterede≠ | 5 | 5 | 3 |
| Resumé≠ | 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). | SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period. | STL Decomposition, introduced by Cleveland, Cleveland, McRae, and Terpenning (1990), is a nonparametric procedure that separates a time series into three additive components — trend, seasonal, and remainder — using iterative locally weighted regression (loess). Widely used in economics, meteorology, and data science, it handles time series of any periodicity and is robust to the presence of outliers, making it a highly flexible alternative to classical decomposition methods. |
| ScholarGateDatasæt ↗ |
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