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	Model ARIMA (Autoregressive Integrated Moving Average)	Konformalno predviđanje za prognoziranje vremenskih serija	Slučajna šuma
Oblast	Ekonometrija	Ekonometrija	Mašinsko učenje
Porodica	Regression model	Regression model	Machine learning
Godina nastanka	2015	2021	2001
Tvorac	Box & Jenkins (Box-Jenkins methodology)	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Breiman, L.
Tip	Univariate time-series model	Distribution-free prediction interval wrapper	Ensemble (bagging of decision trees)
Temeljni izvor	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Drugi nazivi	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
Srodne	5	4	4
Sažetak	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.