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	Predykcja konforemna dla prognozowania szeregów czasowych	N-HiTS
Dziedzina	Ekonometria	Uczenie głębokie
Rodzina	Regression model	Machine learning
Rok powstania	2021	2023
Twórca	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Challu, C. et al.
Typ	Distribution-free prediction interval wrapper	Deep neural forecasting (hierarchical interpolation)
Źródło pierwotne	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.
Inne nazwy	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)	N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation
Pokrewne	4	3
Podsumowanie	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.