قارن الطرق

	نموذج ARIMA (الانحدار الذاتي المتكامل للمتوسط المتحرك)	DeepAR	المُخبِر (Informer)
المجال	الاقتصاد القياسي	التعلم العميق	التعلم العميق
العائلة	Regression model	Machine learning	Machine learning
سنة النشأة	2015	2020	2021
صاحب الطريقة	Box & Jenkins (Box-Jenkins methodology)	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)	Zhou, H. et al.
النوع	Univariate time-series model	Autoregressive recurrent neural network (probabilistic forecasting)	Transformer (ProbSparse self-attention)
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.	Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI.
الأسماء البديلة	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster
ذات صلة	5	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.	Informer is a Transformer-based model introduced by Zhou et al. in 2021 for long-sequence time-series forecasting, using a ProbSparse self-attention mechanism that lowers the computational complexity of the standard Transformer to O(L log L). It is built for problems that demand predictions across thousands of future steps.