| Type: | Package |
| Title: | Joint Segmentation of Correlated Time Series |
| Version: | 0.1.9 |
| Date: | 2018-03-09 |
| Author: | Xavier Collilieux, Emilie Lebarbier and Stephane Robin |
| Maintainer: | Emilie Lebarbier <emilie.lebarbier@agroparistech.fr> |
| Description: | It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <doi:10.48550/arXiv.1505.05660>. |
| License: | GPL-2 |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2018-03-09 13:49:27 UTC; lebarbier |
| Repository: | CRAN |
| Date/Publication: | 2018-03-09 12:59:37 UTC |
Joint Segmentation of Set of Correlated Time-Series
Description
FASeg contains a function designed 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 and a model selection strategy is proposed to determine both the number of breakpoints and the number of factors
Author(s)
Xavier Collilieux, Emilie Lebarbier and Stephane Robin
Maintainer: Emilie Lebarbier <emilie.lebarbier@agroparistech.fr>
References
A factor model approach for the joint segmentation with between-series correlation (arXiv:1505.05660)
Examples
library(FASeg)
data(Y)
M=max(Y$series)
uniKmax=3
multiKmax=11
qmax=M-1
selection=FALSE
WithoutCorr=FALSE
seg=F_FASeg(Y,uniKmax,multiKmax,qmax,selection,WithoutCorr)
Joint Segmentation of Set of Correlated Time-Series
Description
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.
Usage
F_FASeg(Y, uniKmax, multiKmax, qmax, selection, WithoutCorr)
Arguments
Y |
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 |
uniKmax |
Maximal number of segments per series (uniKmax will be lower or equal to n) |
multiKmax |
Maximal number of segments for all the series (multiKmax will be greater or equal to M) |
qmax |
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 |
selection |
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 |
WithoutCorr |
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) |
Value
Contains the following attributes:
SelectedK |
Selected number of segments for all the series if selection=TRUE, the number of segments fixed by the user otherwise (K=multiKmax) |
Selectedq |
Selected number of factors if selection=TRUE, the number of factors fixed by the user otherwise (Q=qmax) |
SelectedSigma |
Estimation of the covariance matrix Sigma |
SelectedPsi |
Estimation of the matrix Psi |
SelectedB |
Estimation of the matrix of coefficients B |
SelectedZ |
Estimation of the latent vectors Z |
SelectedSeg |
Optimal segmentation with a selected or fixed value of the number of segments and the number of factors |
Author(s)
Xavier Collilieux, Emilie Lebarbier and Stephane Robin
References
A factor model approach for the joint segmentation with between-series correlation (arXiv:1505.05660)
Matrix of data
Description
A data frame [(n x M) x 3] containing 5 Gaussian series with size n=50 each simulated as in the paper arXiv:1505.05660 (with rho=0.6 and sigma=0.2). The total number of segments is K=11 or 6 breakpoints (at position 39 for series 1; 35 for series 2; no breaks for series 3; 11 for series 4 and 2, 3 and 12 for series 5).
Usage
data(Y)Format
A data frame with 250 observations on the following 3 variables.
seriesa numeric vector
positiona numeric vector
signala numeric vector
Details
series: the number of the series; position: the grid {1:n}; signal: the values of the observed signal
Examples
library(FASeg)
data(Y)