preprocess: Preprocessing/Postprocessing time series data

In TSPred: Functions for Benchmarking Time Series Prediction

Description

preprocess and postprocess are generic functions for preprocessing and postprocessing time series data, respectively, based on a particular transformation method defined in a processing object. Generally, postprocessing reverses the transformation performed during preprocessing.

Usage

preprocess(obj, ...)

## S3 method for class 'processing'
preprocess(obj, data, ..., map = TRUE)

postprocess(obj, ...)

## S3 method for class 'processing'
postprocess(obj, data, ..., map = TRUE)

Arguments

obj	An object of class processing defining a particular transformation method.
...	Other parameters passed to prep_func/postp_func of obj.
data	A list of time series to be transformed.
map	Should the transformation be performed in each individual time series? If FALSE the function processes the provided set of time series as a whole.

Value

A list containing obj and the transformed time series.

Author(s)

Rebecca Pontes Salles

Examples

data(NN5.A)
t <- LT(base = exp(1))
prep_ts <- preprocess(t,list(NN5.A[,10]))$results[[1]]$res
postp_ts <- postprocess(t,list(prep_ts))$results[[1]]$res

TSPred documentation built on Jan. 21, 2021, 5:10 p.m.