causalcompass.datasets.nonstationary.simulate_nonstationary_var

causalcompass.datasets.nonstationary.simulate_nonstationary_var(p, T, lag=3, sparsity=0.2, sd=0.1, beta_value=1.0, noise_std=1.0, mean_log_sigma=1.0, burn_in=100, seed=0)[source]

Generate VAR data with time-varying noise variance (driven by Gaussian Process).

Parameters:

  • p (int) – Number of variables

  • T (int) – Number of time points

  • lag (int, default 3) – Number of lags in the VAR model

  • sparsity (float, default 0.2) – Sparsity of the causal graph

  • sd (float, default 0.1) – Noise standard deviation

  • beta_value (float, default 1.0) – Coefficient value

  • noise_std (float, default 1.0) – Standard deviation of the GP

  • mean_log_sigma (float, default 1.0) – Mean of the GP

  • burn_in (int, default 100) – Burn-in period

  • seed (int, default 0) – Random seed

Returns:

(data, beta, sigma_t, GC) — time series array of shape (T, p), VAR coefficient matrix, time-varying noise scaling factor of shape (T, p), and ground-truth causal graph of shape (p, p).

Return type:

tuple