| Title: | Event Detection Framework |
|---|---|
| Description: | Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection. |
| Authors: | Margarita Rebolledo [aut],
Sowmya Chandrasekaran [aut, cre],
Frederik Rehbach [aut],
Steffen Moritz [aut] |
| Maintainer: | Sowmya Chandrasekaran <[email protected]> |
| License: | GPL-3 |
| Version: | 0.3.6 |
| Built: | 2025-02-12 04:57:15 UTC |
| Source: | https://github.com/frehbach/eventdetectr |
Detect events/ anomalies in time-series data.
The EventDetectR package enables detection of events/ anomalies in multivariate time-series data. It combines multiple well-known R packages like 'forecast, 'neuralnet' to deliver an easily configurable tool for event detection.
Chandrasekaran S, Rebolledo M, Bartz-Beielstein T (2020).
“EventDetectR – An Open-Source Event Detection System.”
Institute for Data Science, Engineering, and Analytics, TH Köln, Steinmüllerallee 1, 51643 Gummersbach, Germany.
https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9232.
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 )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)## 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)
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
detectEvents( x, windowSize = 100, nIterationsRefit = 1, verbosityLevel = 0, dataPrepators = "ImputeTSInterpolation", dataPreparationControl = list(), buildModelAlgo = "ForecastETS", buildForecastModelControl = list(), buildNeuralNetModelControl = list(), postProcessors = "bedAlgo", postProcessorControl = list(), ignoreVarianceWarning = TRUE )detectEvents( x, windowSize = 100, nIterationsRefit = 1, verbosityLevel = 0, dataPrepators = "ImputeTSInterpolation", dataPreparationControl = list(), buildModelAlgo = "ForecastETS", buildForecastModelControl = list(), buildNeuralNetModelControl = list(), postProcessors = "bedAlgo", postProcessorControl = list(), ignoreVarianceWarning = TRUE )
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 |
edsResults edObject, list of results. $classification -> data.frame containing the T/F event classification
## 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")## 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")
2018s Test set of the gecco industrial challenge - http://www.spotseven.de/gecco/gecco-challenge/
2018s train set of the gecco industrial challenge - http://www.spotseven.de/gecco/gecco-challenge/
Get a list of all data modelling methods that are currently supported in package 'eventDetectR'.
getSupportedModels()getSupportedModels()
allSupportedModels a list of strings with each supported method name. The strings can be copied and used in calls to 'eventDetect' or 'buildEDModel'
models <- getSupportedModels()models <- getSupportedModels()
Get a list of all data postprocessing methods that are currently supported in package 'eventDetectR'.
getSupportedPostProcessors()getSupportedPostProcessors()
allSupportedPostProcessors a list of strings with each supported method name. The strings can be copied and used in calls to 'eventDetect' or 'buildEDModel'
preps <- getSupportedPostProcessors()preps <- getSupportedPostProcessors()
Get a list of all data preparation methods that are currently supported in package 'eventDetectR'.
getSupportedPreparations()getSupportedPreparations()
allSupportedPreparations a list of strings with each supported method name. The strings can be copied and used in calls to 'eventDetect' or 'buildEDModel'
preps <- getSupportedPreparations()preps <- getSupportedPreparations()
Plot an Event Detection Object
## S3 method for class 'edObject' plot(x, varsToPlot = names(edObject$classification), ...)## S3 method for class 'edObject' plot(x, varsToPlot = names(edObject$classification), ...)
x |
edObject |
varsToPlot |
vars |
... |
Additional parameters |
A Plot
Prints the last classification results for an event detection object. If 'nLast' (integer) is given, it specifies the amount of rows to be printed.
## S3 method for class 'edObject' print(x, ...)## S3 method for class 'edObject' print(x, ...)
x |
edObject, the event detection object that shall be printed |
... |
any additional parameters |
Wrapper function for caret::confusionMatrix. qualityStatistics calculates statistics for judging the quality of the eventDetection based on the fitted edModel and a reference dataset
qualityStatistics(edObject, reference)qualityStatistics(edObject, reference)
edObject |
The eventdetection object you obtain by running 'detectEvents' |
reference |
true/false vector, reference vector based on labeled data: which datapoints are real events. |
list, Confusion Matrix and Statistics
train <- geccoIC2018Train[15000:17000,] edObject <- detectEvents(train[,-c(1,11)],windowSize = 1000, nIterationsRefit = 500,verbosityLevel = 2, postProcessorControl = list(nStandardDeviationseventThreshold = 3)) qualityStatistics(edObject, train$EVENT)train <- geccoIC2018Train[15000:17000,] edObject <- detectEvents(train[,-c(1,11)],windowSize = 1000, nIterationsRefit = 500,verbosityLevel = 2, postProcessorControl = list(nStandardDeviationseventThreshold = 3)) qualityStatistics(edObject, train$EVENT)
Simulates Events on columns of a data frame or a matrix by applying different transformations. The events of type sinusoidal, square, binomial or ramp can be used.
simulateEvents( Data, Params, Event_type, Event_strength = NULL, Start_index = NULL, Event_duration = NULL, Percentage = NULL )simulateEvents( Data, Params, Event_type, Event_strength = NULL, Start_index = NULL, Event_duration = NULL, Percentage = NULL )
Data |
Data frame or matrix containing the data to which the events will be introduced |
Params |
Numeric vector or vector of strings indicating the column names (in case Data is a data frame) or the column numbers (in case Data is a matrix) of the parameters in which an event will be simulated |
Event_type |
String vector indicating which type of transformation the parameters will undergo. Current valid options include sinusoidal, square, ramp and slowsinusoidal. If Params contains more that one element and Event_type only contains one element the same transformation will be applied to all given Params |
Event_strength |
(Optional) Numeric Vector indicating the amplitude. Only valid for sinusoidal and square transformations. When specified for other type of transformations it will have no effect. However it must have the same number of elements as Params. |
Start_index |
Numeric, indicates the index where the event should start |
Event_duration |
Numeric, indicates the number of steps the transformation should last. Default is 100 |
Percentage |
(Optional) Numeric value from 0 to 1. Alternative input indicating the percentage of data that should be affected by the transformation. Either Event_duration or Percentage should be especified. |
Matrix or data frame containing the selected columns with simulated events
#Generate event of type sinusoidal and ramp on two columns of the stationBData data set simupar<-c("B_PH_VAL","B_TEMP_VAL") SimulatedEvents<-simulateEvents(stationBData, simupar,Event_type = c("sinusoidal","ramp"), Start_index = 2500) #When specifiying Event_strength the lenght of the vector needs to match the number #of elements in Params. SimulatedEvents<-simulateEvents(stationBData, simupar,Event_type = c("sinusoidal","ramp"), Start_index = 2500, Percentage = 0.2, Event_strength = c(4,1))#Generate event of type sinusoidal and ramp on two columns of the stationBData data set simupar<-c("B_PH_VAL","B_TEMP_VAL") SimulatedEvents<-simulateEvents(stationBData, simupar,Event_type = c("sinusoidal","ramp"), Start_index = 2500) #When specifiying Event_strength the lenght of the vector needs to match the number #of elements in Params. SimulatedEvents<-simulateEvents(stationBData, simupar,Event_type = c("sinusoidal","ramp"), Start_index = 2500, Percentage = 0.2, Event_strength = c(4,1))