This function is dedicated to the joint segmentation (the segmentation is series-specific) in the mean of several correlated series. The form of the correlation is assumed to be arbitrary and we propose to model it with a factor model. A EM algorithm is used to estimate the parameters. A model selection procedure is also proposed to determine both the number of breakpoints and the number of factors.
F_FASeg(Y, uniKmax, multiKmax, qmax, selection, WithoutCorr)Data frame, with size [(n*M) x 3], which contains the data and other informations, n is the length of each series and M is the number of series
Maximal number of segments per series (uniKmax will be lower or equal to n)
Maximal number of segments for all the series (multiKmax will be greater or equal to M)
Maximal number of factors (qmax will be lower or equal to M-1) (default qmax=M-1). If qmax=0 then a joint segmentation with multiKmax segments and without taking into account the correlation between series is performed
A logical value indicating if the selection of the number of segments K and the number of factors Q is performed (default=TRUE). If it is TRUE, K and Q are selected; if it is FALSE, K is fixed to multiKmax and Q is fixed to qmax
A logical value indicating if, when K and Q are selected, the joint segmentation without taking into account the correlation between series is also a possible solution in the selection (default=FALSE)
Contains the following attributes:
Selected number of segments for all the series if selection=TRUE, the number of segments fixed by the user otherwise (K=multiKmax)
Selected number of factors if selection=TRUE, the number of factors fixed by the user otherwise (Q=qmax)
Estimation of the covariance matrix Sigma
Estimation of the matrix Psi
Estimation of the matrix of coefficients B
Estimation of the latent vectors Z
Optimal segmentation with a selected or fixed value of the number of segments and the number of factors
A factor model approach for the joint segmentation with between-series correlation (arXiv:1505.05660)