Exploratory Causal Analysis with Time Series Data (Synthesis Lectures on Data Mining and Knowledge Discovery)
暫譯: 時間序列數據的探索性因果分析(數據挖掘與知識發現綜合講座)

James M. McCracken

  • 出版商: Morgan & Claypool
  • 出版日期: 2016-03-31
  • 售價: $2,090
  • 貴賓價: 9.5$1,986
  • 語言: 英文
  • 頁數: 148
  • 裝訂: Paperback
  • ISBN: 1627059784
  • ISBN-13: 9781627059787
  • 相關分類: Data-mining
  • 海外代購書籍(需單獨結帳)

商品描述

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.

商品描述(中文翻譯)

許多科學領域依賴於系統的觀察數據,對於這些系統,實施控制實驗是困難(或不可能)的。需要數據分析技術來直接從這些觀察數據中識別因果信息和關係。這一需求促使了許多不同的時間序列因果性方法和工具的發展,包括轉移熵(transfer entropy)、收斂交叉映射(convergent cross-mapping, CCM)和Granger因果性統計。實務分析師可以探索文獻,以找到許多識別時間序列數據集中的驅動因素和因果連接的提案。探索性因果分析(Exploratory Causal Analysis, ECA)提供了一個框架,用於探索時間序列數據集中的潛在因果結構,其特點是以短視的目標來確定給定系列中哪些數據系列可能被視為主要驅動因素。在這項工作中,ECA被應用於幾個合成和實證數據集,結果發現所有測試的時間序列因果性工具在許多簡單系統中彼此一致(以及與直觀的因果性概念一致),但在更複雜的系統中可能提供相互矛盾的因果推斷。提出在ECA過程中,不同時間序列因果性工具之間的這種不一致可能提供比其他方式更深入的數據洞察。