detectEvents {EventDetectR}

detectEvents in a given data.frame

Description

detectEvents builds a prediction model (edObject) on the first 'windowSize' points of the given data x. The next 'nIterationRefit' data-points are classified as 'Event' or not. The window is moved iteratively and the next models are fitted. The first 'windowSize' points will always be classified as no Event and should only contain 'clean' data

Usage

detectEvents(
  x,
  windowSize = 100,
  nIterationsRefit = 1,
  verbosityLevel = 0,
  dataPrepators = "ImputeTSInterpolation",
  dataPreparationControl = list(),
  buildModelAlgo = "ForecastETS",
  buildForecastModelControl = list(),
  buildNeuralNetModelControl = list(),
  postProcessors = "bedAlgo",
  postProcessorControl = list(),
  ignoreVarianceWarning = TRUE
)

Arguments

x

data.frame, data which shall be classified as event or not

windowSize

amount of data points to consider in each prediction model

nIterationsRefit

amount of points into the future which will be predicted without fitting a new model. E.g. if nIterationsRefit = 10 then the next five dataPoints are classified without refitting.

verbosityLevel

Print output of function progress. 0 -> No output, 1 -> every 100th model building iteration, 2 -> every 10th, 3 -> every iteration

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()

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 the forecast modelling algo.

buildNeuralNetModelControl

list, control-list containing all additional parameters that shall be passed to the neuralnet modelling algo.

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()

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

Value

edsResults edObject, list of results. $classification -> data.frame containing the T/F event classification

Examples

## Run event detection with default settings:
def <- detectEvents(x = stationBData[1:100,-1])


## Refit the model at every new datapoint,
## have someoutput with verbosityLevel = 2 and ignore
## the variance warning
ed <- detectEvents(stationBData[1:110,-1],nIterationsRefit = 1,
                   verbosityLevel = 2,ignoreVarianceWarning = TRUE)

## Switch to another model: Arima
ed2 <- detectEvents(stationBData[1:110,-1],nIterationsRefit = 1,
                    verbosityLevel = 0,ignoreVarianceWarning = TRUE,
                    buildModelAlgo = "ForecastArima")

   ## Switch to multivariate model: NeuralNetwork
ed3 <- detectEvents(stationBData[1:110,-1],nIterationsRefit = 1, buildModelAlgo = "NeuralNetwork")