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Anomaly detector based on ML regression

Source: R/hanr_ml.R
hanr_ml.Rd

Trains a regression model to forecast the next value from a sliding window and flags large prediction errors as anomalies. Uses DALToolbox regressors.

A set of preconfigured regression methods are described at https://cefet-rj-dal.github.io/daltoolbox/ (e.g., ts_elm, ts_conv1d, ts_lstm, ts_mlp, ts_rf, ts_svm).

Usage

hanr_ml(model, sw_size = 15)

Arguments

model

A DALToolbox regression model.

sw_size

Integer. Sliding window size.

Value

hanr_ml object.

References

  • Hyndman RJ, Athanasopoulos G (2021). Forecasting: Principles and Practice. OTexts.

  • Goodfellow I, Bengio Y, Courville A (2016). Deep Learning. MIT Press.

Examples

library(daltoolbox)
library(tspredit)

# Load anomaly example data
data(examples_anomalies)

# Use a simple example
dataset <- examples_anomalies$simple
head(dataset)
#>       serie event
#> 1 1.0000000 FALSE
#> 2 0.9689124 FALSE
#> 3 0.8775826 FALSE
#> 4 0.7316889 FALSE
#> 5 0.5403023 FALSE
#> 6 0.3153224 FALSE

# Configure a time series regression model
model <- hanr_ml(tspredit::ts_elm(tspredit::ts_norm_gminmax(),
                   input_size=4, nhid=3, actfun="purelin"))

# Fit the model
model <- daltoolbox::fit(model, dataset$serie)

# Run detection
detection <- detect(model, dataset$serie)

# Show detected anomalies
print(detection[(detection$event),])
#>    idx event    type
#> 52  52  TRUE anomaly