ParamMRHLP-class: A Reference Class which contains the parameters of a MRHLP...

In samurais: Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

Description

ParamMRHLP contains all the parameters of a MRHLP model. The parameters are calculated by the initialization Method and then updated by the Method implementing the M-Step of the EM algorithm.

Fields

mData

MData object representing the sample (covariates/inputs X and observed responses/outputs Y).

K

The number of regimes (MRHLP components).

p

The order of the polynomial regression.

q

The dimension of the logistic regression. For the purpose of segmentation, it must be set to 1.

variance_type

Character indicating if the model is homoskedastic (variance_type = "homoskedastic") or heteroskedastic (variance_type = "heteroskedastic"). By default the model is heteroskedastic.

W

Parameters of the logistic process. W = (w_{1},…,w_{K-1}) is a matrix of dimension (q + 1, K - 1), with q the order of the logistic regression. q is fixed to 1 by default.

beta

Parameters of the polynomial regressions. β = (β_{1},…,β_{K}) is an array of dimension (p + 1, d, K), with p the order of the polynomial regression. p is fixed to 3 by default.

sigma2

The variances for the K regimes. If MRHLP model is heteroskedastic (variance_type = "heteroskedastic") then sigma2 is an array of size (d, d, K) (otherwise MRHLP model is homoskedastic (variance_type = "homoskedastic") and sigma2 is a matrix of size (d, d)).

nu

The degree of freedom of the MRHLP model representing the complexity of the model.

phi

A list giving the regression design matrices for the polynomial and the logistic regressions.

Methods

initParam(try_algo = 1)

Method to initialize parameters W, beta and sigma2.

If try_algo = 1 then beta and sigma2 are initialized by segmenting the time series Y uniformly into K contiguous segments. Otherwise, W, beta and sigma2 are initialized by segmenting randomly the time series Y into K segments.

MStep(statMRHLP, verbose_IRLS)

Method which implements the M-step of the EM algorithm to learn the parameters of the MRHLP model based on statistics provided by the object statMRHLP of class StatMRHLP (which contains the E-step).

samurais documentation built on July 28, 2019, 5:02 p.m.