পদ্ধতির তুলনা করুন

	ARIMA (Autoregressive Integrated Moving Average) মডেল	N-HiTS
ক্ষেত্র	অর্থমিতি	গভীর শিখন
পরিবার	Regression model	Machine learning
উদ্ভবের বছর	2015	2023
প্রবর্তক	Box & Jenkins (Box-Jenkins methodology)	Challu, C. et al.
ধরন	Univariate time-series model	Deep neural forecasting (hierarchical interpolation)
মৌলিক উৎস	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	Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI.
অপর নাম	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	N-HiTS — Hiyerarşik İnterpolasyon Tahmini, NHITS, Neural Hierarchical Interpolation
সম্পর্কিত	5	3
সারসংক্ষেপ	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).	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.