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Books > Computing & IT > Applications of computing > Databases > Data mining
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Exploratory Causal Analysis with Time Series Data (Paperback)
Loot Price: R1,381
Discovery Miles 13 810
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Exploratory Causal Analysis with Time Series Data (Paperback)
Series: Synthesis Lectures on Data Mining and Knowledge Discovery
Expected to ship within 10 - 15 working days
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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.
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