方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| ARIMA（自回归积分滑动平均）模型× | 基于案例推理 (CBR)× | |
|---|---|---|
| 领域≠ | 计量经济学 | 软计算 |
| 方法族≠ | Regression model | Machine learning |
| 起源年份≠ | 2015 | 1994 |
| 提出者≠ | Box & Jenkins (Box-Jenkins methodology) | Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle) |
| 类型≠ | Univariate time-series model | Experience-based (analogical) problem solving |
| 开创性文献≠ | 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. DOI ↗ |
| 别名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme |
| 相关≠ | 5 | 2 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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