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007			cr nn 008mamaa
008			220601s2016 sz | s |||| 0|eng d
020			_a9783031019098
024	7		_a10.1007/978-3-031-01909-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA280 _b2016 EB
100	1		_aMcCracken, James M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687230
245	1	0	_aExploratory Causal Analysis with Time Series Data _cby James M. McCracken
250			_a1st edition 2016
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XIII, 133 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2016
776	0	8	_iPrinted edition: _z9783031007811
776	0	8	_iPrinted edition: _z9783031030376
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01909-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI