StreamDC: Condensing Time Series into a Shared Temporal Memory

Time-series dataset condensation compresses a training set into a few synthetic sequences. Under a tight signal storage budget, these sequences must preserve diverse temporal patterns and their useful positional variation. We introduce StreamDC, which learns a class-segmented temporal memory and reuses its signal fragments through overlapping training windows. Linear windows slide across the memory with targets given by temporal class occupancy; class-local cyclic windows wrap from each segment's end to its beginning, adding within-class temporal offsets. All windows jointly optimize the same stored values through an existing teacher-guided condensation objective. After synthesis, denser decoding supplies additional teacher-labeled windows without increasing signal storage. This construction makes temporal reuse part of synthesis and applies across condensation backends. Across five time-series classification datasets, three backends, and two storage budgets, StreamDC achieves higher mean test accuracy than independent-sequence synthesis at equal signal storage in all 30 configurations, with an average gain of 19.5 percentage points. With ShapeCond, StreamDC also averages 12.7 percentage points higher test accuracy than TexTSC across ten dataset–budget settings.