fdars (version 0.3.3)

classify.seasonality: Classify Seasonality Type

Description

Classifies the type of seasonality in functional data. Particularly useful for short series (3-5 years) to identify stable vs variable timing patterns.

Usage

classify.seasonality(
  fdataobj,
  period,
  strength_threshold = NULL,
  timing_threshold = NULL
)

Value

A list with components:

is_seasonal

Logical: is the series seasonal overall?

has_stable_timing

Logical: is peak timing stable across cycles?

timing_variability

Timing variability score (0-1)

seasonal_strength

Overall seasonal strength

cycle_strengths

Per-cycle seasonal strength

weak_seasons

Indices of weak/missing seasons (0-indexed)

classification

One of: "StableSeasonal", "VariableTiming", "IntermittentSeasonal", "NonSeasonal"

peak_timing

Peak timing analysis (if peaks detected)

Arguments

fdataobj

An fdata object.

period

Known seasonal period.

strength_threshold

Threshold for seasonal/non-seasonal (default: 0.3).

timing_threshold

Max std of normalized timing for "stable" (default: 0.05).

Details

Classification types:

• StableSeasonal: Regular peaks with consistent timing

• VariableTiming: Regular peaks but timing shifts between cycles

• IntermittentSeasonal: Some cycles seasonal, some not

• NonSeasonal: No clear seasonality

Examples

# Pure seasonal signal
t <- seq(0, 10, length.out = 500)
X <- matrix(sin(2 * pi * t / 2), nrow = 1)
fd <- fdata(X, argvals = t)

result <- classify.seasonality(fd, period = 2)
print(result$classification)  # "StableSeasonal"