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Empirical Identification of Non-stationary Dynamics in Time Series of Recordings

  • Emili Balaguer-Ballester
  • Alejandro Tabas-Diaz
  • Marcin Budka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8779)

Abstract

Non-stationarity time series are very common in physical, biological and in real-world systems in general, ranging from geophysics, econometrics or electroencephalography to logistics. Identifying, detecting and adapting learning algorithms to non-stationary environments is a fundamental task in many data mining scenarios; however it is often a major challenge for current methodologies. Data analysis in the context of time-varying statistical moments is a very active research direction in machine learning and in computational statistics; but theoretical insights into latent causes of non-stationarity in empirical data are very scarce. In this study, we evaluate the capacity of the trajectory classification error statistic in order to detect a significant variation in the underlying dynamics of data collected in multiple stages. We analysed qualitatively the conditions leading to observable changes in non-stationary data generated by Duffing non-linear oscillators; which are ubiquitous models of complex classification problems. Analyses are further benchmarked in a dataset consisting of atmospheric pollutants time series.

Keywords

Non-stationarity non-autonomous dynamics phase space reconstruction high dimensional spaces Duffing oscillator trial-to-trial variability 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Emili Balaguer-Ballester
    • 2
  • Alejandro Tabas-Diaz
    • 1
  • Marcin Budka
    • 1
  1. 1.Faculty of Science and TechnologyBournemouth UniversityPooleUK
  2. 2.Bernstein Center for Computational Neuroscience Heidelberg-MannheimUniversity of HeidelbergUK

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