000 03316nam a22005055i 4500
001 978-3-031-01909-8
003 DE-He213
005 20240730163747.0
007 cr nn 008mamaa
008 220601s2016 sz | s |||| 0|eng d
020 _a9783031019098
_9978-3-031-01909-8
024 7 _a10.1007/978-3-031-01909-8
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aMcCracken, James M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980422
245 1 0 _aExploratory Causal Analysis with Time Series Data
_h[electronic resource] /
_cby James M. McCracken.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXIII, 133 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
505 0 _aPreface -- Acknowledgments -- Introduction -- Causality Studies -- Time Series Causality Tools -- Exploratory Causal Analysis -- Conclusions -- Bibliography -- Author's Biography.
520 _aMany 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.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_980423
650 2 4 _aStatistics.
_914134
710 2 _aSpringerLink (Online service)
_980424
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031007811
776 0 8 _iPrinted edition:
_z9783031030376
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_980425
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01909-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c84958
_d84958