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)

Value

An object of class processing.

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

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

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.