Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection Articles uri icon

publication date

  • January 2018

issue

  • 1

volume

  • 20

international standard serial number (ISSN)

  • 1099-4300

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.

keywords

  • entropy
    stochastic process
    minimum-entropy sets
    anomaly detection
    functional data