hanr_ml: Anomaly detector based on ML regression

In harbinger: A Unified Time Series Event Detection Framework

Anomaly detector based on ML regression

Description

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)

# 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),])

harbinger documentation built on Feb. 11, 2026, 9:06 a.m.