قارن الطرق

	نموذج ARIMA (الانحدار الذاتي المتكامل للمتوسط المتحرك)	DeepAR
المجال	الاقتصاد القياسي	التعلم العميق
العائلة	Regression model	Machine learning
سنة النشأة	2015	2020
صاحب الطريقة	Box & Jenkins (Box-Jenkins methodology)	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)
النوع	Univariate time-series model	Autoregressive recurrent neural network (probabilistic forecasting)
المصدر التأسيسي	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.
الأسماء البديلة	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR
ذات صلة	5	5
الملخص	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.