Information Theoretic Estimators for Dependence in Time Series
نویسنده
چکیده
Recently, there has been much interest in designing and analyzing estimators for information theoretic functionals of probability density functions, under the assumption of independent and identically distributed (IID) data. However, estimators designed for IID data fail to capture relevant information when presented with time series data. We first present information theoretic functionals which take into account temporal dependence patterns and discuss methods for estimating these functionals. We then present an empirical evaluation of these estimators, in which we study the abilities of different estimators to detect dependencies in different types of synthetic data.
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