RemixAutoML (version 0.4.2)

WideTimeSeriesEnsembleForecast: WideTimeSeriesEnsembleForecast

Description

WideTimeSeriesEnsembleForecast to generate forecasts and ensemble data

Usage

WideTimeSeriesEnsembleForecast(
  TS_Models = c("arima", "tbats", "nnet"),
  ML_Methods = c("CatBoost", "XGBoost", "H2oGBM", "H2oDRF"),
  Path = "C:/Users/aantico/Documents/Package",
  TargetName = "Weekly_Sales",
  DateName = "Date",
  NTrees = 750,
  TaskType = "GPU",
  GridTune = FALSE,
  MaxNumberModels = 5
)

Arguments

TS_Models

Select which ts model forecasts to ensemble

ML_Methods

Select which models to build for the ensemble

Path

The path to the folder where the ts forecasts are stored

TargetName

"Weekly_Sales"

DateName

"Date"

NTrees

Select the number of trees to utilize in ML models

TaskType

GPU or CPU

GridTune

Set to TRUE to grid tune the ML models

MaxNumberModels

The number of models to try for each ML model

See Also

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