Detection of trend changes in time series using bayesian inference.

@article{Schtz2011DetectionOT,
  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},
  year={2011},
  volume={84 2 Pt 1},
  pages={
          021120
        }
}
  • 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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References

SHOWING 1-10 OF 72 REFERENCES
Multiscale spectral analysis for detecting short and long range change points in time series
TLDR
The method works well to detect change points and illustrates the importance of analysing the time series on different time horizons, which are observed to be statistically significant. Expand
Detection of Undocumented Changepoints: A Revision of the Two-Phase Regression Model
TLDR
This paper examines detection and adjustment of climatic series for undocumented changepoint times, primarily from single site data, and a simple and easily applicable revision of this statistical method is introduced. Expand
Break function regression
The break is a continuous function consisting of two linear parts. It serves as a regression model for trend changes in time series. A typical application field of such a model is climatology. WeExpand
Exact and efficient Bayesian inference for multiple changepoint problems
TLDR
The method can cope with a range of models, and exact simulation from the posterior distribution is possible in a matter of minutes, and can be useful within an MCMC algorithm, even when the independence assumptions do not hold. Expand
Detecting Abrupt Changes by Wavelet Methods
The objective of this paper is to contribute to the methodology available for dealing with the detection and the estimation of the location of discontinuities in one-dimensional piecewise smoothExpand
An objective Bayesian analysis of the change point problem
The Bayesian literature on the change point problem deals with the inference of a change in the distribution of a set of time-ordered data based on a sample of fixed size. This is the so-calledExpand
Detection of weak transitions in signal dynamics using recurrence time statistics
Signal detection in noisy and nonstationary environments is very challenging. In this Letter, we study why the two types of recurrence times [Phys. Rev. Lett. 83 (1999) 3178] may be very useful forExpand
The problem of the Nile: Conditional solution to a changepoint problem
SUMMARY Inference is considered for the point in a sequence of random variables at which the probability distribution changes. An approximation to the conditional distribution of the maximumExpand
Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance
In this paper we develop wavelet methods for detecting and estimating jumps and cusps in the mean function of a non-parametric regression model. An important characteristic of the model consideredExpand
Complex network approach for recurrence analysis of time series
We propose a novel approach for analysing time series using complex network theory. We identify the recurrence matrix (calculated from time series) with the adjacency matrix of a complex network andExpand
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