buildEDModel: build Event Detection Model
In EventDetectR: Event Detection Framework

Description Usage Arguments Value Examples

Builds an event detection object (edObject) containing all models and configurations that are used to detect events in given data.

buildEDModel(
  x,
  dataPrepators = "ImputeTSInterpolation",
  dataPreparationControl = list(),
  buildModelAlgo = "ForecastETS",
  buildForecastModelControl = list(),
  buildNeuralNetModelControl = list(),
  postProcessors = "bedAlgo",
  postProcessorControl = list(),
  ignoreVarianceWarning = FALSE,
  oldModel = NULL
)

`x`	data.frame containing initial data on which the model will be fitted. Data should be free of events. The data should not include a timestamp column
`dataPrepators`	string or vector of strings, that defines which preparators to use. Lists are not accepted. Usage Example: dataPreparators = "ImputeTSInterpolation" results in the usage of imputeTS::na.interpolation as a data preparator. All possible preparators are listed via: getSupportedPreparations() Can also be set to NULL in order to shut off data preparation
`dataPreparationControl`	list, control-list containing all additional parameters that shall be passed to the dataPreparators.
`buildModelAlgo`	string, model name to be used. All possible preparators are listed via: getSupportedModels().
`buildForecastModelControl`	list, control-list containing all additional parameters that shall be passed to forecast modeling algorithm
`buildNeuralNetModelControl`	list, control-list containing all additional parameters that shall be passed to the neuralnet modeling algorithm
`postProcessors`	string or vector of strings, that defines which postProcessors to use. Lists are not accepted. Usage Example: postProcessors = "bedAlgo" results in the usage of bed as a event postProcessing tool. All possible preparators are listed via: getSupportedPostProcessors() Can also be set to NULL in order to shut off data postProcessing
`postProcessorControl`	list, control-list containing all additional parameters that shall be passed to the postProcessirs.
`ignoreVarianceWarning`	Ignores the continously appearing warning for missing variance in some variable columns given a smaller windowSize
`oldModel`	If another model was previously fitted it can be passed to the next model fit. By doing so the eventHistory is preserved

model, event detection object (edObject) containing all models and configurations that are used to detect events in given data.

## build a simple event detection model with standard configuration
x <- stationBData[100:200,-1]
buildEDModel(x,ignoreVarianceWarning = TRUE)

## Set up a more complex event detection model defining some additional configuration
buildEDModel(x, buildModelAlgo = "ForecastArima",ignoreVarianceWarning = TRUE)

 ## Set up a multivariate neuralnetwork model
buildEDModel(x, buildModelAlgo = "NeuralNetwork",ignoreVarianceWarning = TRUE)