R: Time series transformation methods

LT {TSPred}

R Documentation

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.

Author(s)

Rebecca Pontes Salles

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.