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	ऑटोरेग्रेसिव इंटीग्रेटेड मूविंग एवरेज (ARIMA) मॉडल	रैंडम फ़ॉरेस्ट
क्षेत्र	अर्थमिति	मशीन अधिगम
परिवार	Regression model	Machine learning
उद्भव वर्ष	2015	2001
प्रवर्तक	Box & Jenkins (Box-Jenkins methodology)	Breiman, L.
प्रकार	Univariate time-series model	Ensemble (bagging of decision trees)
मौलिक स्रोत	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	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
उपनाम	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
संबंधित	5	4
सारांश	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).	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.