Functional Model-Based Discrimination and Clustering using wavelets

The R package funHDDCwavelet allows to perform time series clustering, via discrete wavelet transform, and modeling wavelet coefficients with a parcimonious gaussian mixture model. Parameter estimation is done with an EM algorithm.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Erwangf/funHDDC-wavelet")

Example

This is a basic example which shows you how to solve a common problem:

library(funHDDCwavelet)
#>    __             _   _ ____  ____   ____                         _      _   
#>   / _|_   _ _ __ | | | |  _ \|  _ \ / ___|_      ____ ___   _____| | ___| |_ 
#>  | |_| | | | '_ \| |_| | | | | | | | |   \ \ /\ / / _` \ \ / / _ \ |/ _ \ __|
#>  |  _| |_| | | | |  _  | |_| | |_| | |___ \ V  V / (_| |\ V /  __/ |  __/ |_ 
#>  |_|  \__,_|_| |_|_| |_|____/|____/ \____| \_/\_/ \__,_| \_/ \___|_|\___|\__|
#> 
#> 
#> 
## basic example code

Acknowledgements

This research benefited from the support of the FMJH ’Program Gaspard Monge for optimization and operations research and their interactions with data science’, and from the support from EDF and Thales.