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| ARIMA (Autoregressive Integrated Moving Average) Model× | Konform forudsigelse til tidsserieprognoser× | Random Forest× | |
|---|---|---|---|
| Fagområde≠ | Økonometri | Økonometri | Maskinlæring |
| Familie≠ | Regression model | Regression model | Machine learning |
| Oprindelsesår≠ | 2015 | 2021 | 2001 |
| Ophavsperson≠ | Box & Jenkins (Box-Jenkins methodology) | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) | Breiman, L. |
| Type≠ | Univariate time-series model | Distribution-free prediction interval wrapper | Ensemble (bagging of decision trees) |
| 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 | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Aliasser≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relaterede≠ | 5 | 4 | 4 |
| 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). | Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023). | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
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