摘要翻译：
本文研究了大量随机过程之间的内在相依性和相似性的识别问题。用线性模型来描述时间序列之间的关系，用与相应建模误差相关的能量来量化它们之间的相似性。这种方法可以用图论来解释，当一个过程提供最好的模型来解释另一个过程时，它提出了一种自然的方法来将过程分组在一起。此外，本文介绍的聚类技术将证明是文献中描述的其他多元过程的动态推广。
---
英文标题：
《Econometrics as Sorcery》
---
作者：
G. Innocenti and D. Materassi
---
最新提交年份：
2008
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--
一级分类：Physics        物理学
二级分类：Chaotic Dynamics        混沌动力学
分类描述：Dynamical systems, chaos, quantum chaos, topological dynamics, cycle expansions, turbulence, propagation
动力系统，混沌，量子混沌，拓扑动力学，循环展开，湍流，传播
--

---
英文摘要：
  The paper deals with the problem of identifying the internal dependencies and similarities among a large number of random processes. Linear models are considered to describe the relations among the time series and the energy associated to the corresponding modeling error is the criterion adopted to quantify their similarities. Such an approach is interpreted in terms of graph theory suggesting a natural way to group processes together when one provides the best model to explain the other. Moreover, the clustering technique introduced in this paper will turn out to be the dynamical generalization of other multivariate procedures described in literature.
---
PDF链接：
https://arxiv.org/pdf/0801.3047