مقایسهٔ روش‌ها

	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)	پیش‌بینی انطباقی برای پیش‌بینی سری‌های زمانی	N-HiTS
حوزه	اقتصادسنجی	اقتصادسنجی	یادگیری عمیق
خانواده	Regression model	Regression model	Machine learning
سال پیدایش	2015	2021	2023
پدیدآور	Box & Jenkins (Box-Jenkins methodology)	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Challu, C. et al.
نوع	Univariate time-series model	Distribution-free prediction interval wrapper	Deep neural forecasting (hierarchical interpolation)
منبع بنیادین	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	Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.	Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI.
نام‌های دیگر	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)	N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation
مرتبط	5	4	3
خلاصه	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).	Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023).	N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting), introduced by Challu and colleagues in 2023, is a deep neural forecasting architecture that combines the hierarchical forecasts of multiple stacks operating at different sampling rates and merges them through interpolation. It extends N-BEATS to deliver markedly better accuracy on long forecast horizons.