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 M. Holschneider},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  volume={84 2 Pt 1},
  • Nadine 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… CONTINUE READING
    9 Citations

    Figures, Tables, and Topics from this paper

    Explore Further: Topics Discussed in This Paper

    Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling
    • PDF
    Detecting a trend change in cross-border epidemic transmission
    • Y. Maeno
    • Mathematics, Biology
    • Physica A: Statistical Mechanics and its Applications
    • 2016
    • 5
    • PDF
    Analyzing long-term data from biological surveys
    • 1
    • PDF
    Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
    • 28
    • PDF


    Multiscale spectral analysis for detecting short and long range change points in time series
    • 19
    • PDF
    Break function regression
    • 32
    • PDF
    Exact and efficient Bayesian inference for multiple changepoint problems
    • 367
    • PDF