Порівняння методів

	Модель ARIMA (Авторегресійна інтегрована ковзна середня)	DeepAR	Випадковий ліс
Галузь	Економетрика	Глибоке навчання	Машинне навчання
Родина	Regression model	Machine learning	Machine learning
Рік появи	2015	2020	2001
Автор методу	Box & Jenkins (Box-Jenkins methodology)	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)	Breiman, L.
Тип	Univariate time-series model	Autoregressive recurrent neural network (probabilistic forecasting)	Ensemble (bagging of decision trees)
Основоположне джерело	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	Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Інші назви	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Пов'язані	5	5	4
Підсумок	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).	DeepAR is Amazon's industrial forecasting model, introduced by Salinas, Flunkert and Gasthaus (2017; published 2020), that uses an autoregressive recurrent neural network to estimate the parameters of a probability distribution at each step, producing a confidence interval rather than a single point forecast. It can model many related time series jointly within one model.	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.