Synthetic Time Series Generation via Complex Networks
The Inverse Quantile Graph (InvQG) framework generates synthetic time series using complex network mappings based on transition dynamics in quantile space. It successfully preserves marginal distributions and short-term temporal dependencies across many models, offering a transparent and efficient alternative to GANs. However, it struggles to capture long-range or higher-order temporal dynamics due to its reliance on first-order Markov transitions.