repr_matrix: Computation of matrix of representations from matrix of time series

Description

The repr_matrix computes matrix of representations from matrix of time series

Usage

repr_matrix(
  x,
  func = NULL,
  args = NULL,
  normalise = FALSE,
  func_norm = norm_z,
  windowing = FALSE,
  win_size = NULL
)

Arguments

the matrix, data.frame or data.table of time series, where time series are in rows of the table

func

the function that computes representation

args

the list of additional (or required) parameters of func (function that computes representation)

normalise

normalise (scale) time series before representations computation? (default is FALSE)

func_norm

the normalisation function (default is norm_z)

windowing

perform windowing? (default is FALSE)

win_size

the size of the window

Value

the numeric matrix of representations of time series

Details

This function computes representation to an every row of a matrix of time series and returns matrix of time series representations. It can be combined with windowing (see repr_windowing) and normalisation of time series.

Examples

Run this code

# NOT RUN {
# Create random matrix of time series
mat_ts <- matrix(rnorm(100), ncol = 10)
repr_matrix(mat_ts, func = repr_paa,
 args = list(q = 5, func = meanC))

# return normalised representations, and normalise time series by min-max normalisation
repr_matrix(mat_ts, func = repr_paa,
 args = list(q = 2, func = meanC), normalise = TRUE, func_norm = norm_min_max)

# with windowing
repr_matrix(mat_ts, func = repr_feaclip, windowing = TRUE, win_size = 5)

# }