One Batch Is Enough: A Unified Dataset Condensation Framework for General Time Series Analysis

Time-series analysis is critical in real-world applications, yet the explosion of time-series data imposes severe burdens on storage and computational resources. Recently, dataset condensation has emerged as a promising data-centric solution by synthesizing compact yet informative datasets to replace large-scale raw data. However, existing methods are largely vision-centric, failing to capture unique temporal properties of time series, or task-specific, tightly coupling the condensed data to a particular downstream objective. As a result, these approaches suffer from feature mismatch and fail to generalize across diverse time-series tasks. To bridge this gap, we propose UniTSC, the first unified dataset condensation framework for general time-series analysis. UniTSC employs a multi-view hybrid encoder to capture task-invariant representations across temporal, spectral, and topological perspectives. Building upon this representation, we design a tri-space alignment paradigm that jointly aligns optimization trajectories, power spectral densities, and multivariate dependency structures, enabling comprehensive information preservation under extreme compression. Extensive experiments show that UniTSC retains up to 97.9\% of downstream performance using as little as 0.01\% of the original training data, within our experimental settings tied to standard sequence lengths, revealing that a single batch-equivalent budget ($\textless$ 128 samples) is sufficient to capture the essential dynamics of complex time-series data.

Time-series data is everywhere in modern life, from weather records to machine sensor readings. However, sensors often collect data nonstop, making the resulting datasets large, repetitive, and costly to store or use for model training. This raises a practical question: can we replace a huge time-series dataset with a tiny one while still keeping the important information needed for many different tasks? We propose UniTSC, a method that turns a large time-series dataset into a very small synthetic one. Rather than keeping only task-specific clues, it preserves three kinds of useful behavior: how signals change over time, how patterns repeat, and how different variables influence one another. In the experiments, models trained on only one small batch of synthetic data kept most of the performance of models trained on the full dataset. This can make time-series AI faster, cheaper, and easier to use in resource-limited settings.