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	ऑटोरेग्रेसिव इंटीग्रेटेड मूविंग एवरेज (ARIMA) मॉडल	सूचनाकर्ता
क्षेत्र	अर्थमिति	गहन अधिगम
परिवार	Regression model	Machine learning
उद्भव वर्ष	2015	2021
प्रवर्तक	Box & Jenkins (Box-Jenkins methodology)	Zhou, H. et al.
प्रकार	Univariate time-series model	Transformer (ProbSparse self-attention)
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	Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI.
उपनाम	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster
संबंधित	5	5
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).	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.