transformation_methods function

Time series transformation methods

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

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

Returns

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()

Author(s)

Rebecca Pontes Salles