CounterfacTS can be utilized beyond time-series forecasting in various ways. One potential application is in anomaly detection, where counterfactuals can help identify unusual patterns or outliers in the data. By creating counterfactual scenarios and comparing them to the original data, anomalies that deviate significantly from normal behavior can be detected. Additionally, CounterfacTS can be used in causal inference to understand the impact of different variables on outcomes. By manipulating input features and observing changes in predictions, causal relationships between variables can be inferred. Furthermore, CounterfacTS could also aid in model explainability by visualizing how specific features influence predictions, helping users understand the inner workings of complex models.