hanr_emd: Anomaly detector using EMD

In harbinger: A Unified Time Series Event Detection Framework

Anomaly detector using EMD

Description

Empirical Mode Decomposition (CEEMD) to extract intrinsic mode functions and flag anomalies from high-frequency components. Wraps hht::CEEMD.

Usage

hanr_emd(noise = 0.1, trials = 5)

Arguments

noise	Numeric. Noise amplitude for CEEMD.
trials	Integer. Number of CEEMD trials.

Value

hanr_emd object

References

• Huang NE, et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Royal Society A.

Examples

library(daltoolbox)

# Load anomaly example data
data(examples_anomalies)

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

# Configure EMD-based anomaly detector
model <- hanr_emd()

# 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.