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ParallelAutoTBATS to run the 4 data sets at once
ParallelAutoTBATS( Output, MetricSelection = "MAE", TrainValidateShare = c(0.5, 0.5), NumCores = max(1L, min(4L, parallel::detectCores() - 2L)) )
The output returned from TimeSeriesDataPrepare()
Choose from MAE, MSE, and MAPE
The value returned from TimeSeriesPrepare()
Default of max(1L, min(4L, parallel::detectCores())). Up to 4 cores can be utilized.
Time series data sets to pass onto auto modeling functions
Other Time Series Helper: FinalBuildArfima(), FinalBuildArima(), FinalBuildETS(), FinalBuildNNET(), FinalBuildTBATS(), FinalBuildTSLM(), GenerateParameterGrids(), OptimizeArfima(), OptimizeArima(), OptimizeETS(), OptimizeNNET(), OptimizeTBATS(), OptimizeTSLM(), ParallelAutoARIMA(), ParallelAutoArfima(), ParallelAutoETS(), ParallelAutoNNET(), ParallelAutoTSLM(), PredictArima(), RL_Performance(), Regular_Performance(), StackedTimeSeriesEnsembleForecast(), TimeSeriesDataPrepare(), WideTimeSeriesEnsembleForecast()
FinalBuildArfima()
FinalBuildArima()
FinalBuildETS()
FinalBuildNNET()
FinalBuildTBATS()
FinalBuildTSLM()
GenerateParameterGrids()
OptimizeArfima()
OptimizeArima()
OptimizeETS()
OptimizeNNET()
OptimizeTBATS()
OptimizeTSLM()
ParallelAutoARIMA()
ParallelAutoArfima()
ParallelAutoETS()
ParallelAutoNNET()
ParallelAutoTSLM()
PredictArima()
RL_Performance()
Regular_Performance()
StackedTimeSeriesEnsembleForecast()
TimeSeriesDataPrepare()
WideTimeSeriesEnsembleForecast()
# NOT RUN { ParallelAutoTBATS( MetricSelection = "MAE", Output = NULL, TrainValidateShare = c(0.50,0.50), NumCores = max(1L, min(4L, parallel::detectCores()-2L))) # }
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