# hmatr: Calculate the heterogeneity matrix. In Rssa: A Collection of Methods for Singular Spectrum Analysis

## Description

Function calculates the heterogeneity matrix for the one-dimensional series.

## Usage

 1 2 3 4 5 6 7 8 hmatr(F, ..., B = N %/% 4, T = N %/% 4, L = B %/% 2, neig = 10) ## S3 method for class 'hmatr' plot(x, col = rev(heat.colors(256)), main = "Heterogeneity Matrix", xlab = "", ylab = "", ...) 

## Arguments

 F the series to be checked for structural changes ... further arguments passed to ssa routine for hmatr call or image for plot.hmatr call B integer, length of base series T integer, length of tested series L integer, window length for the decomposition of the base series neig integer, number of eigentriples to consider for calculating projections x 'hmatr' object col color palette to use main plot title xlab,ylab labels for 'x' and 'y' axis

## Details

The heterogeneity matrix (H-matrix) provides a consistent view on the structural discrepancy between different parts of the series. Denote by F_{i,j} the subseries of F of the form: F_{i,j} = ≤ft(f_{i},…,f_{j}\right). Fix two integers B > L and T ≥q L. Let these integers denote the lengths of base and test subseries, respectively. Introduce the H-matrix G_{B,T} with the elements g_{ij} as follows:

g_{ij} = g(F_{i,i+B}, F_{j,j+T}),

for i=1,…,N-B+1 and j=1,…,N-T+1, that is we split the series F into subseries of lengths B and T and calculate the heterogeneity index between all possible pairs of the subseries.

The heterogeneity index g(F^{(1)}, F^{(2)}) between the series F^{(1)} and F^{(2)} can be calculated as follows: let U_{j}^{(1)}, j=1,…,L denote the eigenvectors of the SVD of the trajectory matrix of the series F^{(1)}. Fix I to be a subset of ≤ft\{1,…,L\right\} and denote \mathcal{L}^{(1)} = \mathrm{span}\,≤ft(U_{i},\, i \in I\right). Denote by X^{(2)}_{1},…,X^{(2)}_{K_{2}} (K_{2} = N_{2} - L + 1) the L-lagged vectors of the series F^{(2)}. Now define

g(F^{(1)},F^{(2)}) = \frac{∑_{j=1}^{K_{2}}{\mathrm{dist}\,^{2}≤ft(X^{(2)}_{j}, \mathcal{L}^{(1)}\right)}} {∑_{j=1}^{K_{2}}{≤ft\|X^{(2)}_{j}\right\|^{2}}},

where \mathrm{dist}\,(X,\mathcal{L}) denotes the Euclidean distance between the vector X and the subspace \mathcal{L}. One can easily see that 0 ≤q g ≤q 1.

## Value

object of type 'hmatr'

## References

Golyandina, N., Nekrutkin, V. and Zhigljavsky, A. (2001): Analysis of Time Series Structure: SSA and related techniques. Chapman and Hall/CRC. ISBN 1584881941

ssa
 1 2 3 4 # Calculate H-matrix for co2 series h <- hmatr(co2, L = 24) # Plot the matrix plot(h)