Vertaile menetelmiä
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| Aikasarjojen ristiinvalidointi (liukuva/laajeneva ikkuna)× | ARIMA (Autoregressive Integrated Moving Average) -malli× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe≠ | Process / pipeline | Regression model |
| Syntyvuosi≠ | 2012 | 2015 |
| Kehittäjä≠ | Christoph Bergmeir & José Benítez | Box & Jenkins (Box-Jenkins methodology) |
| Tyyppi≠ | Forecast evaluation procedure | Univariate time-series model |
| Alkuperäislähde≠ | Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213. 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 |
| Rinnakkaisnimet≠ | Rolling-Origin Cross-Validation, Walk-Forward Validation, Expanding Window Evaluation, Zaman Serisi Çapraz Doğrulama | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Liittyvät≠ | 3 | 5 |
| Tiivistelmä≠ | Time-series cross-validation is a resampling procedure designed for sequentially ordered data. Instead of randomly partitioning observations — which would destroy temporal structure and introduce data leakage — it advances a forecast origin one step at a time, fitting a model on all past data up to that origin and evaluating it on the immediately following out-of-sample period. Economists, financial analysts, and meteorologists use it whenever an honest, operationally realistic estimate of predictive accuracy is required for a time-ordered process. | 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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