Exploratory Causal Analysis with Time Series Data (Record no. 84958)
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| fixed length control field | 03316nam a22005055i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-01909-8 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163747.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2016 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031019098 |
| -- | 978-3-031-01909-8 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.312 |
| 100 1# - AUTHOR NAME | |
| Author | McCracken, James M. |
| 245 10 - TITLE STATEMENT | |
| Title | Exploratory Causal Analysis with Time Series Data |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2016. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 133 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Data Mining and Knowledge Discovery, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Preface -- Acknowledgments -- Introduction -- Causality Studies -- Time Series Causality Tools -- Exploratory Causal Analysis -- Conclusions -- Bibliography -- Author's Biography. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01909-8 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
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| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2016. |
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| -- | computer |
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| -- | rdamedia |
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| -- | online resource |
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| -- | text file |
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| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Statistics . |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Statistics. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2151-0075 |
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| -- | ZDB-2-SXSC |
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