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.

254 Downloads

Updated 3 Sep 2021

View License

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: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01970/full.
**************************************************************************
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) (https://www.mathworks.com/matlabcentral/fileexchange/80380-pattern-transition-detection-algorithm-ptda), MATLAB Central File Exchange. Retrieved .

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

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!