Сравнение методов

	Модель ARIMA (авторегрессионная интегрированная скользящая средняя)	DeepAR	Информер
Область	Эконометрика	Глубокое обучение	Глубокое обучение
Семейство	Regression model	Machine learning	Machine learning
Год появления	2015	2020	2021
Автор метода	Box & Jenkins (Box-Jenkins methodology)	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)	Zhou, H. et al.
Тип	Univariate time-series model	Autoregressive recurrent neural network (probabilistic forecasting)	Transformer (ProbSparse self-attention)
Основополагающий источник	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.
Другие названия	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
Связанные	5	5	5
Сводка	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.