Anomaly detector using EMD — hanr

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)
#>       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 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),])
#>    idx event    type
#> 49  49  TRUE anomaly