complexity: Calculate Dynamic Complexity Measures for Time-Series Data

In codyna: Complex Dynamic Systems

Calculate Dynamic Complexity Measures for Time-Series Data

Description

Computes dynamic complexity and other rolling window measures for univariate time series data.

Usage

complexity(data, measures = "complexity", window = 7L, align = "center")

Arguments

data	[ts, numeric()] Univariate time series data.
measures	[character()] A vector of measures to calculate. See 'Details' for more information on the available measures.
window	[integer(1)] A positive integer specifying the rolling window size. Must be at least 2 (default: 7).
align	[character(1)] Alignment of the window. The available options are: "center" (default), "right", and "left". The calculated measure is assigned to the center, rightmost, or leftmost point of the window, respectively.

Details

The following measures can be calculated:

• "complexity": Product of fluctuation and distribution measures.

• "fluctuation": Root mean square of successive differences.

• "distribution": Deviation from uniform distribution.

• "autocorrelation": Lag-1 autocorrelation coefficient.

• "max": Rolling maximum.

• "min": Rolling minimum.

• "variance": Rolling variance.

The option "all" computes all of the above.

Value

A tibble with the time index, the original time-series data, and the calculated measures.

Examples

set.seed(123)
ts_data <- stats::arima.sim(list(order = c(1, 1, 0), ar = 0.6), n = 200)

# Single measure
comp_single <- complexity(ts_data, measures = "complexity")

# Multiple measures
comp_multi <- complexity(ts_data, measures = c("complexity", "variance"))

codyna documentation built on Jan. 16, 2026, 5:15 p.m.