Anomaly detector using ARIMA — hanr

Fits an ARIMA model to the series and flags observations with large model residuals as anomalies. Wraps ARIMA from the forecast package.

Usage

hanr_arima()

Value

hanr_arima object.

Details

The detector estimates ARIMA(p,d,q) and computes standardized residuals. Residual magnitudes are summarized via a distance function and thresholded with outlier heuristics from harutils().

References

Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2015). Time Series Analysis: Forecasting and Control. Wiley.

Examples

library(daltoolbox)

# 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 ARIMA anomaly detector
model <- hanr_arima()

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

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

# Show detected anomalies
print(detection[(detection$event),])
#>    idx event    type
#> 50  50  TRUE anomaly