Publication:
Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection

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2018-05-01
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Abstract
We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to estimate minimum entropy sets. These sets are relevant to detect anomalous or outlier functional data. A numerical experiment illustrates the performance of the proposed method; in addition, we conduct an analysis of mortality rate curves as an interesting application in a real-data context to explore functional anomaly detection.
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entropy, stochastic process, minimum-entropy sets, anomaly detection, functional data
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