causalcompass.datasets.vanilla.simulate_var

causalcompass.datasets.vanilla.simulate_var(p, T, lag=3, sparsity=0.2, beta_value=1.0, sd=0.1, burn_in=100, seed=0)

Generate time series data from a Vector Autoregressive (VAR) model.

References

https://github.com/iancovert/Neural-GC

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

• beta_value (float, default 1.0) – Coefficient value

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

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

• seed (int, default 0) – Random seed

Returns:

(data, beta, GC) — time series array of shape (T, p), coefficient matrix, and ground-truth causal graph of shape (p, p).

Return type:

tuple