השוואת שיטות

	מודל ARIMA (Autoregressive Integrated Moving Average)	הנמקה מבוססת-מקרה (CBR)
תחום	אקונומטריקה	מחשוב רך
משפחה	Regression model	Machine learning
שנת המקור	2015	1994
הוגה השיטה	Box & Jenkins (Box-Jenkins methodology)	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)
סוג	Univariate time-series model	Experience-based (analogical) problem solving
מקור מכונן	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.
כינויים	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
קשורות	5	2
תקציר	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.