Description: Exploratory Causal Analysis with Time Series Data by James M. McCracken Estimated delivery 3-12 business days Format Paperback Condition Brand New Description 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. Publisher Description 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. Author Biography James McCracken received his B.S. in Physics and B.S. in Astrophysics from the Florida Institute of Technology in 2004, his M.S. from the University of Central Florida in 2006, and his Ph.D. in Physics from George Mason University in 2015. He currently lives and works in the Washington, D.C., metro area. Details ISBN 3031007816 ISBN-13 9783031007811 Title Exploratory Causal Analysis with Time Series Data Author James M. McCracken Format Paperback Year 2016 Pages 133 Publisher Springer International Publishing AG GE_Item_ID:151447813; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
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End Time: 2024-11-20T03:11:48.000Z
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ISBN-13: 9783031007811
Book Title: Exploratory Causal Analysis with Time Series Data
Number of Pages: Xiii, 133 Pages
Language: English
Publication Name: Exploratory Causal Analysis with Time Series Data
Publisher: Springer International Publishing A&G
Publication Year: 2016
Subject: Probability & Statistics / General, Databases / Data Mining
Item Weight: 10.3 Oz
Type: Textbook
Author: James M. Mccracken
Subject Area: Mathematics, Computers
Item Length: 9.3 in
Series: Synthesis Lectures on Data Mining and Knowledge Discovery Ser.
Item Width: 7.5 in
Format: Trade Paperback
