TSPred (version 5.1)

LT: Time series transformation methods

Description

Constructors for the processing class representing a time series processing method based on a particular time series transformation.

Usage

LT(base = exp(1))
BoxCoxT(lambda = NULL, prep_par = NULL, postp_par = NULL, ...)
WT(
  level = NULL,
  filter = NULL,
  boundary = "periodic",
  prep_par = NULL,
  postp_par = NULL,
  ...
)
subsetting(train_perc = 0.8, test_len = NULL)
SW(window_len = NULL)
NAS(na.action = stats::na.omit, prep_par = NULL)
MinMax(min = NULL, max = NULL, byRow = TRUE)
AN(min = NULL, max = NULL, byRow = TRUE, outlier.rm = TRUE, alpha = 1.5)
DIFF(
  lag = NULL,
  differences = NULL,
  type = "simple",
  postp_par = list(addinit = FALSE)
)
MAS(order = NULL, prep_par = NULL, postp_par = list(addinit = FALSE))
PCT(postp_par = NULL)
EMD(num_imfs = 0, meaningfulImfs = NULL, prep_par = NULL)

Arguments

base

LogT

lambda

See BCT

prep_par

List of named parameters required by prep_func.

postp_par

List of named parameters required by postp_func.

...

Other parameters to be encapsulated in the class object.

level

See WaveletT

filter

See WaveletT

boundary

See WaveletT

train_perc

See train_test_subset

test_len

See train_test_subset

window_len

See sw

na.action

Function for handling missing values in time series data

min

See an

max

See an

byRow

See an

outlier.rm

See an

alpha

See an

lag

See Diff

differences

See Diff

type

See Diff

order

See mas

num_imfs

See emd

meaningfulImfs

See emd

Value

An object of class processing.

Mapping-based nonstationary transformation methods

LT: Logarithmic transform. prep_func set as LogT and postp_func set as LogT.rev.

BoxCoxT: Box-Cox transform. prep_func set as BCT and postp_func set as BCT.rev.

DIFF: Differencing. prep_func set as Diff and postp_func set as Diff.rev.

MAS: Moving average smoothing. prep_func set as mas and postp_func set as mas.rev.

PCT: Percentage change transform. prep_func set as pct and postp_func set as pct.rev.

Splitting-based nonstationary transformation methods

WT: Wavelet transform. prep_func set as WaveletT and postp_func set as WaveletT.rev.

EMD: Empirical mode decomposition. prep_func set as emd and postp_func set as emd.rev.

Data subsetting methods

subsetting: Subsetting data into training and testing sets. prep_func set as train_test_subset and postp_func set to NULL.

SW: Sliding windows. prep_func set as sw and postp_func set to NULL.

Methods for handling missing values

NAS: Missing values treatment. prep_func set as parameter na.action and postp_func set to NULL.

Normalization methods

MinMax: MinMax normalization. prep_func set as minmax and postp_func set to minmax.rev.

AN: Adaptive normalization. prep_func set as an and postp_func set to an.rev.

References

R. Salles, K. Belloze, F. Porto, P.H. Gonzalez, and E. Ogasawara. Nonstationary time series transformation methods: An experimental review. Knowledge-Based Systems, 164:274-291, 2019.

See Also

Other constructors: ARIMA(), MSE_eval(), evaluating(), modeling(), processing(), tspred()