Hallo! Tracked shipping to Netherlands with Delivery Duty Paid for just €7 

Ship to
Netherlands
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Exploratory Causal Analysis with Time Series Data
Type
Physical Book
Author
Publisher
Language
English
Pages
133
Format
Paperback
Dimensions
23.5 x 19.1 x 0.8 cm
Weight
0.27 kg.
ISBN13
9783031007811
Edition No.
1
Categories

Exploratory Causal Analysis with Time Series Data

James M. McCracken (Author) · Springer · Paperback

Exploratory Causal Analysis with Time Series Data - McCracken, James M.

Cheaper New Book Imported to Netherlands
Delivery: 02 Sep - 09 Sep Shipping: 12 to 16 business days.
€ 50,12
Faster New Book Imported to Netherlands
Delivery: 20 Aug - 24 Aug Shipping: 4 to 5 business days.
€ 61,87
Import costs and 9% BTW included in the price ✅
€ 50,12

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Exploratory Causal Analysis with Time Series Data"

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews