How does the performance of TSDiff compare to other unconditional time series diffusion models (e.g., TSP) on

Abstract

Abstract: Demand forecasting in competitive, uncertain business environments requires models that can integrate multiple evaluation perspectives rather than being restricted to hyperparameter optimization based on a single metric. This traditional approach tends to prioritize one error indicator, which can bias results when metrics provide contradictory signals. In this context, the Hierarchical Evaluation Function (HEF) is proposed as a multi-metric framework for hyperparameter optimization that integrates explanatory power (R2), sensitivity to extreme errors (RMSE), and average accuracy (MAE). The per

Research Question

How does the performance of TSDiff compare to other unconditional time series diffusion models (e.g., TSP) on the Monash benchmark datasets in terms of forecasting accuracy and sample diversity?

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