The Power of the Pruned Exact Linear Time(PELT) Test in Multiple Changepoint Detection

@article{Wambui2015ThePO,
  title={The Power of the Pruned Exact Linear Time(PELT) Test in Multiple Changepoint Detection},
  author={Gachomo Dorcas Wambui and Gichuhi Anthony Waititu and Anthony Kibira Wanjoya},
  journal={American Journal of Theoretical and Applied Statistics},
  year={2015},
  volume={4},
  pages={581}
}
Changepoint detection is the problem of estimating the point at which the statistical properties of a sequence of observations change. Over the years several multiple changepoint search algorithms have been proposed to overcome this challenge. They include binary segmentation algorithm, the Segment neighbourhood algorithm and the Pruned Exact Linear Time (PELT) algorithm. The PELT algorithm is exact and under mild conditions has a computational cost that is linear in the number of data points… Expand

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References

SHOWING 1-10 OF 28 REFERENCES
Efficient detection of multiple changepoints within an oceanographic time series
structures. Detecting the presence of changepoints in oceanographic time-series is of particular importance as statistical and engineering modelling of the ocean environment, structural loading andExpand
Optimal detection of changepoints with a linear computational cost
TLDR
This work considers the problem of detecting multiple changepoints in large data sets and introduces a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost which is linear in the number of observations. Expand
A fast Bayesian change point analysis for the segmentation of microarray data
TLDR
A new implementation of the Bayesian change point method that is O(n) in both speed and memory is presented; bcp 2.1 runs in approximately 45 s on a single processor with a sequence of length 10,000--a tremendous speed gain. Expand
BAYESIAN ESTIMATION OF THE NUMBER OF CHANGE POINTS
The problem of estimating the number of change points in a sequence of independent random variables is considered in a Bayesian framework. We find that, under mild assumptions and with respect to aExpand
Bayesian Estimation of the Number of Change Points in Simple Linear Regression Models
A Bayesian approach is considered to detect the number of change points in simple linear regression models. A normal-gamma empirical prior for the regression parameters based on maximum likelihoodExpand
Deciphering behavioral changes in animal movement with a “multiple change point algorithm- classification tree” framework
TLDR
A straightforward analytical framework based on a recent multiple change point algorithm: the PELT algorithm, a dynamic programming pruning search method to find, within time series, the optimal combination of number and locations of change points is proposed. Expand
A Review and Comparison of Changepoint Detection Techniques for Climate Data
TLDR
It is shown that the common trend TPR and Sawa’s Bayes criteria procedures seem optimal for most climate time series, whereas the SNH procedure and its nonparametric variant are probably best for mostClimate time series. Expand
An algorithm for optimal partitioning of data on an interval
TLDR
This letter describes a simple but powerful algorithm that searches the exponentially large space of partitions of N data points in time O(N/sup 2/), which is guaranteed to find the exact global optimum. Expand
Detection of multiple changes in a sequence of dependent variables
We present some results of convergence for a minimum contrast estimator in a problem of change-points estimation. Here, we consider that the changes affect the marginal distribution of a sequence ofExpand
Application of modified information criterion to multiple change point problems
The modified information criterion (MIC) is applied to detect multiple change points in a sequence of independent random variables. We find that the method is consistent in selecting the correctExpand
...
1
2
3
...