Pattern Transition Detection Algorithm (PTDA)

version 1.1.3 (997 KB) by Kathrin Viol
This algorithm is an extension of the change point analysis to detect general changes in the pattern of a time series.


Updated 3 Sep 2021

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The algorithm calculates the Time Frequency Distribution (TFD), Recurrence Plot (RP), and Dynamic Complexity (DC) of a time series and applies the change point analysis to the original as well as to the time series/matrices of TFD, RP and DC.
Details can be found in our accompanying paper (submitted for publication). The idea of the algorithm is presented in this article:
Updates Version 1.1
- outliers are removed from the input time series
- function "findchangepts" is used instead of "ischange"
- the original time series is also assessed for changes of a linear trend
- recurrence plots, dynamic complexity, and time frequency distributions are not assessed for changes of the variance anymore (only changes of the mean)
- omitted outlier deletion of the resulting change points
- cross-validation omitted (not necessary anymore)
- peakfinder instead of mean used to determine overall transition point
- added visualization for the results
- added example data

Cite As

Kathrin Viol (2022). Pattern Transition Detection Algorithm (PTDA) (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2018b
Compatible with R2018b
Platform Compatibility
Windows macOS Linux

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