run_rolling_xi_ccf: Rolling Multivariate Xi-CCF Analysis

In xiacf: Quantifying Nonlinear Dependence and Lead-Lag Dynamics via Chatterjee's Xi

Rolling Multivariate Xi-CCF Analysis

Description

Performs a rolling window analysis using Chatterjee's Xi cross-correlation to assess the time-varying non-linear lead-lag relationship between two time series.

Usage

run_rolling_xi_ccf(
  x,
  y,
  time_index = NULL,
  window_size,
  step_size = 1,
  max_lag = 20,
  n_surr = 100,
  bidirectional = TRUE,
  sig_level = 0.95,
  n_cores = NULL,
  save_dir = NULL
)

Arguments

x	A numeric vector representing the first time series (predictor/lead candidate).
y	A numeric vector representing the second time series (response/lag candidate).
time_index	Optional vector of timestamps (e.g., Date, POSIXct) corresponding to x and y.
window_size	An integer specifying the size of the rolling window.
step_size	An integer specifying the step size by which the window is shifted. Default is 1.
max_lag	An integer specifying the maximum positive lag to compute.
n_surr	An integer specifying the number of MIAAFT surrogate datasets for the null hypothesis test.
bidirectional	Logical. If TRUE (default), computes both "X leads Y" and "Y leads X".
sig_level	A numeric value specifying the significance level for the confidence intervals. Default is 0.95.
n_cores	An integer specifying the number of cores for parallel execution. If NULL, runs sequentially.
save_dir	A character string specifying the directory path to save intermediate window results as RDS files. If NULL (default), results are not saved to disk.

Value

A data.frame containing the rolling window results in a tidy long-format.

xiacf documentation built on April 16, 2026, 5:08 p.m.