RemixAutoML (version 0.4.6)

OptimizeETS: OptimizeETS

Description

OptimizeETS is a function that takes raw data and returns the necessary time series data and objects for model building. It also fills any time gaps with zeros. Use this before you run any time series model functions.

Usage

OptimizeETS(
  Output,
  Path = NULL,
  MetricSelection = "MAE",
  DataSetName = NULL,
  train = NULL,
  test = NULL,
  FullData = NULL,
  HoldOutPeriods = NULL,
  MinVal = NULL,
  TargetName = NULL,
  DateName = NULL,
  TrainValidateShare = NULL,
  FinalGrid = NULL
)

Arguments

Output

This is passed through as output from TimeSeriesDataPrepare() and passed through ParallelArima()

Path

Path to where you want the model and xregs saved. Leave NULL to not save.

MetricSelection

Select from "MSE", "MAE", or "MAPE"

DataSetName

This is the name of the data set passed through in parallel loop

train

Training data returned from TimeSeriesDataPrepare()

test

Test data returned from TimeSeriesDataPrepare()

FullData

Full series data for scoring and ensemble

HoldOutPeriods

Holdout periods returned from TimeSeriesDataPrepare()

MinVal

Minimum value of target variable returned from TimeSeriesDataPrepare()

TargetName

Target variable name returned from TimeSeriesDataPrepare()

DateName

Date variable name returned from TimeSeriesDataPrepare()

TrainValidateShare

A two-element numeric vector. The first element is the weight applied to the training performance and the remainder is applied to the validation performance.

FinalGrid

Grid for forecasting models

Value

Time series data sets to pass onto auto modeling functions

See Also

Other Time Series Helper: FinalBuildArfima(), FinalBuildArima(), FinalBuildETS(), FinalBuildNNET(), FinalBuildTBATS(), FinalBuildTSLM(), GenerateParameterGrids(), OptimizeArfima(), OptimizeArima(), OptimizeNNET(), OptimizeTBATS(), OptimizeTSLM(), ParallelAutoARIMA(), ParallelAutoArfima(), ParallelAutoETS(), ParallelAutoNNET(), ParallelAutoTBATS(), ParallelAutoTSLM(), PredictArima(), RL_Performance(), Regular_Performance(), StackedTimeSeriesEnsembleForecast(), TimeSeriesDataPrepare(), WideTimeSeriesEnsembleForecast()

Examples

# NOT RUN {
Results <- OptimizeETS(
  Output,
  Path = NULL,
  MetricSelection = "MAE",
  DataSetName = NULL,
  train = NULL,
  test = NULL,
  FullData = NULL,
  HoldOutPeriods = NULL,
  MinVal = NULL,
  TargetName = NULL,
  DateName = NULL,
  TrainValidateShare = NULL,
  FinalGrid = NULL)
# }