Fast Change Point Detection via Sequential Gradient Descent
Find change points efficiently in AR() models
Find change points efficiently in ARIMA(, , ) models
Find change points efficiently in ARMA(, ) models
Find change points efficiently in logistic regression models
Wrapper functions for fastcpd
Find change points efficiently in GARCH(, ) models
Find change points efficiently in penalized linear regression models
Find change points efficiently in linear regression models
Find change points efficiently in mean change models
Find change points efficiently in mean variance change models
Find change points efficiently in Poisson regression models
Find change points efficiently in time series data
Find change points efficiently in VAR() models
Find change points efficiently in variance change models
An S4 class to store the output created with fastcpd()
Find change points efficiently
Plot the data and the change points for a fastcpd object
Print the call and the change points for a fastcpd object
Show the available methods for a fastcpd object
Show the summary of a fastcpd object
Variance estimation for ARMA model with change points
Variance estimation for linear models with change points
Variance estimation for mean change models
Variance estimation for median change models
Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function.
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