  Dataset condensation is a newborn technique that generates a small dataset
that can be used in training deep neural networks to lower training costs. 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). This challenge arises from disparities in the evaluation of
synthetic data. In classification, the synthetic data is considered
well-distilled if the model trained with the full dataset and the model trained
with the synthetic dataset yield identical labels for the same input,
regardless of variations in output logits distribution. Conversely, in
TS-forecasting, the effectiveness of synthetic data distillation is determined
by the distance between predictions of the two models. The synthetic data is
deemed well-distilled only when all data points within the predictions are
similar. Consequently, TS-forecasting has a more rigorous evaluation
methodology compared to classification. To mitigate this gap, we theoretically
analyze the optimization objective of dataset condensation for TS-forecasting
and propose a new one-line plugin of dataset condensation designated as Dataset
Condensation for Time Series Forecasting (CondTSF) based on our analysis.
Plugging CondTSF into previous dataset condensation methods facilitates a
reduction in the distance between the predictions of the model trained with the
full dataset and the model trained with the synthetic dataset, thereby
enhancing performance. We conduct extensive experiments on eight commonly used
time series datasets. CondTSF consistently improves the performance of all
previous dataset condensation methods across all datasets, particularly at low
condensing ratios.
