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| ARIMA (Autoregressive Integrated Moving Average) 모형× | X-13ARIMA-SEATS 계절 조정× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 2015 | 1998 |
| 창시자≠ | Box & Jenkins (Box-Jenkins methodology) | U.S. Census Bureau; Findley et al. |
| 유형≠ | Univariate time-series model | Non-parametric / model-based hybrid |
| 원전≠ | 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 | Findley, D. F., Monsell, B. C., Bell, W. R., Otto, M. C., & Chen, B.-C. (1998). New capabilities and methods of the X-12-ARIMA seasonal adjustment program. Journal of Business & Economic Statistics, 16(2), 127–152. DOI ↗ |
| 별칭≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | X-13ARIMA-SEATS, X-12-ARIMA, Census X-13, Mevsimsel Düzeltme X-13 |
| 관련≠ | 5 | 3 |
| 요약≠ | 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). | X-13ARIMA-SEATS is the standard seasonal adjustment program produced by the U.S. Census Bureau, combining RegARIMA pre-adjustment with either the classical X-11 filter or the model-based SEATS signal-extraction algorithm. It is the official tool used by national statistical agencies worldwide — including Eurostat and the U.S. Bureau of Labor Statistics — to remove recurring calendar and seasonal patterns from monthly or quarterly economic time series such as GDP, employment, and retail sales. |
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