RemixAutoML (version 0.4.5)

AutoDiffLagN: AutoDiffLagN

Description

AutoDiffLagN create differences for selected numerical columns

Usage

AutoDiffLagN(
  data,
  DateVariable = NULL,
  GroupVariables = NULL,
  DiffVariables = NULL,
  NLag = 1L,
  Sort = FALSE,
  RemoveNA = TRUE
)

Arguments

data

Source data

DateVariable

Date column used for sorting

GroupVariables

Difference data by group

DiffVariables

Column names of numeric columns to difference

NLag

Number of rows back for the lag operation

Sort

TRUE to sort your data inside the function

RemoveNA

Set to TRUE to remove rows with NA generated by the lag operation

See Also

Other Feature Engineering: AutoDataPartition(), AutoHierarchicalFourier(), AutoInteraction(), AutoLagRollStatsScoring(), AutoLagRollStats(), AutoTransformationCreate(), AutoTransformationScore(), AutoWord2VecModeler(), AutoWord2VecScoring(), ContinuousTimeDataGenerator(), CreateCalendarVariables(), CreateHolidayVariables(), DT_GDL_Feature_Engineering(), DifferenceDataReverse(), DifferenceData(), DummifyDT(), H2OAutoencoderScoring(), H2OAutoencoder(), ModelDataPrep(), Partial_DT_GDL_Feature_Engineering(), TimeSeriesFill()

Examples

# NOT RUN {
# Create fake data
data <- RemixAutoML::FakeDataGenerator(
  Correlation = 0.70,
  N = 50000,
  ID = 2L,
  FactorCount = 3L,
  AddDate = TRUE,
  ZIP = 0L,
  TimeSeries = FALSE,
  ChainLadderData = FALSE,
  Classification = FALSE,
  MultiClass = FALSE)

# Store Cols to diff
Cols <- names(data)[which(unlist(data[, lapply(.SD, is.numeric)]))]

# Clean data before running AutoDiffLagN
data <- RemixAutoML::ModelDataPrep(data = data, Impute = FALSE, CharToFactor = FALSE, FactorToChar = TRUE)

# Run function
data <- RemixAutoML::AutoDiffLagN(
  data,
  DateVariable = "DateTime",
  GroupVariables = c("Factor_1", "Factor_2"),
  DiffVariables = Cols,
  NLag = 1,
  Sort = TRUE,
  RemoveNA = TRUE)
# }
# NOT RUN {
# }