hanr_remd: Anomaly detector using REMD

In harbinger: A Unified Time Series Event Detection Framework

Anomaly detector using REMD

Description

Anomaly detection using REMD The EMD model adjusts to the time series. Observations distant from the model are labeled as anomalies. It wraps the EMD model presented in the forecast library.

Usage

hanr_remd(noise = 0.1, trials = 5)

Arguments

noise	nosie
trials	trials

Value

hanr_remd object

References

• Souza, J., Paixão, E., Fraga, F., Baroni, L., Alves, R. F. S., Belloze, K., Dos Santos, J., Bezerra, E., Porto, F., Ogasawara, E. REMD: A Novel Hybrid Anomaly Detection Method Based on EMD and ARIMA. Proceedings of the International Joint Conference on Neural Networks, 2024. doi:10.1109/IJCNN60899.2024.10651192

Examples

library(daltoolbox)

# Load anomaly example data
data(examples_anomalies)

# Use a simple example
dataset <- examples_anomalies$simple
head(dataset)

# Configure REMD detector
model <- hanr_remd()

# Fit the model
model <- 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.