Detection of trend changes in time series using bayesian inference.

  title={Detection of trend changes in time series using bayesian inference.},
  author={Nadine Sch{\"u}tz and Matthias Holschneider},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  volume={84 2 Pt 1},
  • N. Schütz, M. Holschneider
  • Published 2011
  • Physics, Medicine
  • Physical review. E, Statistical, nonlinear, and soft matter physics
Change points in time series are perceived as isolated singularities where two regular trends of a given signal do not match. The detection of such transitions is of fundamental interest for the understanding of the system's internal dynamics or external forcings. In practice observational noise makes it difficult to detect such change points in time series. In this work we elaborate on a bayesian algorithm to estimate the location of the singularities and to quantify their credibility. We… Expand
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