So sánh phương pháp

	Mô hình Dự báo Xám GM(1,1)	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Lập luận dựa trên trường hợp (CBR)
Lĩnh vực	Tính toán mềm	Kinh tế lượng	Tính toán mềm
Họ	Regression model	Regression model	Machine learning
Năm ra đời	1982	2015	1994
Người khởi xướng	Julong Deng	Box & Jenkins (Box-Jenkins methodology)	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)
Loại	Small-sample grey forecasting model	Univariate time-series model	Experience-based (analogical) problem solving
Công trình gốc	Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.	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	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.
Tên gọi khác	GM(1,1), grey prediction model, grey forecasting, gri tahmin modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
Liên quan	2	5	2
Tóm tắt	GM(1,1) is the core forecasting model of grey system theory, introduced by Julong Deng in 1982, designed to predict from very few observations and incomplete information — situations where classical time-series models like ARIMA need far more data. It accumulates the raw series to expose a hidden exponential trend, fits a first-order grey differential equation, and projects future values, making it popular in engineering, energy, and management forecasting with short data records.	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).	Case-based reasoning solves a new problem by retrieving similar problems solved in the past and adapting their solutions, rather than reasoning from first principles or a trained statistical model. Formalized as the Retrieve-Reuse-Revise-Retain cycle by Aamodt and Plaza in 1994 and popularized by Janet Kolodner, CBR mirrors how human experts in medicine, law, and engineering reason by analogy from remembered cases, and it learns simply by storing each newly solved case.