The objective of dataset condensation is to ensure that the model trained with the synthetic dataset can perform comparably to the model trained with full datasets. However, existing methods predominantly concentrate on classification tasks, posing challenges in their adaptation to time series forecasting (TS-forecasting).

It is crucial for accelerating network training and reducing data storage requirements.

D ataset C ondensation ( EDC), establishes a benchmark for both small and large-scale dataset condensation.

The synthetic datasets are expected to capture the essence of the knowledge contained in real-world datasets such that the former yields a similar performance as the latter.