fdars (version 0.3.3)

cfd.autoperiod: CFDAutoperiod: Clustered Filtered Detrended Autoperiod

Description

Implements the CFDAutoperiod algorithm (Puech et al. 2020) which applies first-order differencing for detrending, then uses density-based clustering of spectral peaks and ACF validation on the original signal.

Usage

cfd.autoperiod(fdataobj, cluster_tolerance = 0.1, min_cluster_size = 1)

Value

A list of class "cfd_autoperiod_result" with components:

period

Primary detected period (best validated cluster center)

confidence

Combined confidence (ACF validation * spectral power)

acf_validation

ACF validation score for the primary period

n_periods

Number of validated period clusters

periods

All detected period cluster centers

confidences

Confidence scores for each detected period

Arguments

fdataobj

An fdata object.

cluster_tolerance

Relative tolerance for clustering nearby period candidates. Default: 0.1 (10% relative difference). Candidates within this tolerance are grouped into clusters.

min_cluster_size

Minimum number of candidates required to form a valid cluster. Default: 1.

Details

The CFDAutoperiod algorithm works in five stages:

1. Differencing: First-order differencing removes polynomial trends

2. FFT: Computes periodogram on the detrended signal

3. Peak Detection: Finds all peaks above the noise floor

4. Clustering: Groups nearby period candidates into clusters

5. ACF Validation: Validates cluster centers using the ACF of the original (non-differenced) signal

This method is particularly effective for:

• Signals with strong polynomial trends

• Multiple concurrent periodicities

• Signals where spectral leakage creates nearby spurious peaks

References

Puech, T., Boussard, M., D'Amato, A., & Millerand, G. (2020). A fully automated periodicity detection in time series. In Advanced Analytics and Learning on Temporal Data (pp. 43-54). Springer.

See Also

autoperiod for the original Autoperiod algorithm, sazed for an ensemble method

Examples

# Generate data with trend
t <- seq(0, 20, length.out = 400)
X <- matrix(0.2 * t + sin(2 * pi * t / 2), nrow = 1)
fd <- fdata(X, argvals = t)

# CFDAutoperiod handles trends via differencing
result <- cfd.autoperiod(fd)
print(result)

# Multiple periods detected
X2 <- matrix(sin(2 * pi * t / 2) + 0.5 * sin(2 * pi * t / 5), nrow = 1)
fd2 <- fdata(X2, argvals = t)
result2 <- cfd.autoperiod(fd2)
print(result2$periods)  # All detected periods