fdars (version 0.3.3)

detect.period: Seasonal Analysis Functions for Functional Data

Description

Functions for analyzing seasonal patterns in functional data including period estimation, peak detection, seasonal strength measurement, and detection of seasonality changes. Detect Period with Multiple Methods

Usage

detect.period(
  fdataobj,
  method = c("sazed", "autoperiod", "cfd", "fft", "acf"),
  ...
)

Value

A period detection result. The exact class depends on the method:

sazed_result

For SAZED method

autoperiod_result

For Autoperiod method

cfd_autoperiod_result

For CFD method

period_estimate

For FFT and ACF methods

Arguments

fdataobj

An fdata object.

method

Detection method to use:

"sazed"

SAZED ensemble (default) - parameter-free, most robust

"autoperiod"

Autoperiod - hybrid FFT + ACF with gradient ascent

"cfd"

CFDAutoperiod - differencing + clustering + ACF validation

"fft"

Simple FFT periodogram peak

"acf"

Simple ACF peak detection

...

Additional arguments passed to the underlying method:

tolerance

For SAZED: relative tolerance for voting (default 0.1)

n_candidates

For Autoperiod: max FFT peaks to consider (default 5)

gradient_steps

For Autoperiod: refinement steps (default 10)

cluster_tolerance

For CFD: clustering tolerance (default 0.1)

min_cluster_size

For CFD: minimum cluster size (default 1)

max_lag

For ACF: maximum lag to search

detrend_method

For FFT/ACF: "none", "linear", or "auto"

Details

Unified interface for period detection that dispatches to specialized algorithms based on the chosen method. Provides a single entry point for all period detection functionality.

Method selection guidance:

• sazed: Best general-purpose choice. Combines 5 methods and uses voting - robust across signal types with no tuning needed.

• autoperiod: Good when you need candidate details and gradient-refined estimates. Slightly more precise than SAZED.

• cfd: Best for signals with strong polynomial trends. Also detects multiple concurrent periods.

• fft: Fastest, but sensitive to noise and trends.

• acf: Simple but effective for clean periodic signals.

See Also

sazed, autoperiod, cfd.autoperiod, estimate.period, detect.periods

Examples

# Generate seasonal data
t <- seq(0, 20, length.out = 400)
X <- matrix(sin(2 * pi * t / 2) + 0.1 * rnorm(400), nrow = 1)
fd <- fdata(X, argvals = t)

# Default (SAZED) - most robust
result <- detect.period(fd)
print(result$period)

# Autoperiod with custom settings
result <- detect.period(fd, method = "autoperiod", n_candidates = 10)

# CFDAutoperiod for trended data
X_trend <- matrix(0.3 * t + sin(2 * pi * t / 2), nrow = 1)
fd_trend <- fdata(X_trend, argvals = t)
result <- detect.period(fd_trend, method = "cfd")

# Simple FFT for speed
result <- detect.period(fd, method = "fft")