VARDetect (version 0.1.8)

simu_var: Generate VAR(p) model data with break points

Description

This function is used for generate simulated time series

Usage

simu_var(
  method = c("sparse", "group sparse", "fLS", "LS"),
  nob = 300,
  k = 20,
  lags = 1,
  lags_vector = NULL,
  brk,
  sigma = NULL,
  skip = 50,
  spectral_radius = 0.98,
  seed = NULL,
  sp_density = NULL,
  group_mats = NULL,
  group_index = NULL,
  group_type = c("columnwise", "rowwise"),
  sparse_mats = NULL,
  sp_pattern = c("off-diagonal", "diagonal", "random"),
  rank = NULL,
  info_ratio = NULL,
  signals = NULL,
  singular_vals = NULL
)

Value

A list object, which contains the followings

series

matrix of timeseries data

noises

matrix of noise term data

sparse_mats

list of sparse matrix in the transition matrix

lowrank_mats

list of low-rank matrix in the transition matrix

Arguments

method

the structure of time series: "sparse","group sparse", "fLS", "LS"

nob

sample size

k

dimension of transition matrix

lags

lags of VAR time series. Default is 1.

lags_vector

a vector of lags of VAR time series for each segment

brk

a vector of break points with (nob+1) as the last element

sigma

the variance matrix for error term

skip

an argument to control the leading data points to obtain a stationary time series

spectral_radius

to ensure the time series is piecewise stationary.

seed

an argument to control the random seed. Default seed is 1.

sp_density

if we choose random pattern, we should provide the sparsity density for each segment

group_mats

transition matrix for group sparse case

group_index

group index for group lasso.

group_type

type for group lasso: "columnwise", "rowwise". Default is "columnwise".

sparse_mats

transition matrix for sparse case

sp_pattern

a choice of the pattern of sparse component: diagonal, 1-off diagonal, random, custom

rank

if we choose method is low rank plus sparse, we need to provide the ranks for each segment

info_ratio

the information ratio leverages the signal strength from low rank and sparse components

signals

manually setting signal for each segment (including sign)

singular_vals

singular values for the low rank components

Examples

nob <- (10^3 * 4) # number of time points
p <- 15 # number of time series components
brk <- c(floor(nob / 3), floor(2 * nob / 3), nob + 1)
m0 <- length(brk) - 1 # number of break points
q.t <- 2 # the true AR order
m <- m0 + 1 # number of segments
sp_density <- rep(0.05, m * q.t) # sparsity level (5%)
try <- simu_var("sparse", nob = nob, k = p, lags = q.t, brk = brk,
                sp_pattern = "random", sp_density = sp_density)
print(plot_matrix(do.call("cbind", try$model_param), m * q.t))