So sánh phương pháp

	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Lập luận dựa trên trường hợp (CBR)
Lĩnh vực	Kinh tế lượng	Tính toán mềm
Họ	Regression model	Machine learning
Năm ra đời	2015	1994
Người khởi xướng	Box & Jenkins (Box-Jenkins methodology)	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)
Loại	Univariate time-series model	Experience-based (analogical) problem solving
Công trình gốc	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	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
Liên quan	5	2
Tóm tắt	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.