ParamMPWR-class: A Reference Class which contains the parameters of a MPWR...

In fchamroukhi/MPWR_r: Multiple Polynomial Piecewise Regression ('MPWR') for Optimal Multivariate Time-Series Segmentation

Description

ParamMPWR contains all the parameters of a MPWR model. The parameters are calculated by the initialization Method and then updated by the Method dynamic programming (here dynamic programming)

Fields

X

Numeric vector of length m representing the covariates/inputs x_{1},…,x_{m}.

Y

Numeric vector of length m representing the observed response/output y_{1},…,y_{m}.

m

Numeric. Length of the response/output vector Y.

K

The number of regimes (PWR components).

p

The order of the polynomial regression. p is fixed to 3 by default.

gamma

Set of transition points. gamma is a column matrix of size (K + 1, 1).

beta

Parameters of the polynomial regressions. beta is an array of dimension (p + 1, d, K), with p the order of the polynomial regression, d the dimension of the multivariate time-series. p is fixed to 3 by default.

sigma2

The variances for the K regimes. sigma2 is an array of size (d, d, K).

phi

A matrix giving the regression design matrix for the polynomial regression.

Methods

computeDynamicProgram(C1, K)

Method which implements the dynamic programming based on the cost matrix C1 and the number of regimes/segments K.

computeParam()

Method which estimates the parameters beta and sigma2 knowing the transition points gamma.

fchamroukhi/MPWR_r documentation built on April 24, 2020, 12:39 p.m.