Downsampling Time Series via Auto-Regression

High-resolution time series are essential for granular analysis but often prohibitively expensive to process. While low-resolution representations offer a pragmatic alternative, conventional direct downsampling acts as a destructive projection that irreversibly disrupts latent dynamical structures, leading to significant performance collapse in downstream tasks. We challenge this long-standing hegemony by proposing that the transition to lower resolutions should be an active, distribution-aware synthesis rather than a passive, deterministic pruning. To realize this paradigm shift, we introduce VARes, a framework that reformulates downsampling as a hierarchical auto-regressive process. Empirical results demonstrate that data obtained with VARes consistently outperforms traditionally downsampled counterparts by amplifying task-critical features and effectively denoising signals. Our work establishes a new downsampling standard, proving that our method yields data that is better suited for machine learning models on downstream time series tasks than conventional downsampling approaches.