FlashRocket: Archive-Scale Time Series Classification in Seconds

As wearables and the Internet of Things proliferate, AI systems will increasingly need to process many different sensors at once and adapt quickly, ideally within seconds, as those sensors drift. One time series foundation model may seem like the natural answer, but it still needs a classification head trained for every task. In our experiments, training and testing these classifiers takes from 15 minutes to an estimated 7.5 hours, far too slow for quick adaptation. Training and testing hundreds of classifiers in seconds on one GPU is hard: the tasks differ by orders of magnitude in size, length and channel count, so most are too small to keep the GPU busy while a few are large enough to stall the rest. We present FlashRocket, a system that trains and tests classifiers on MiniRocket features at archive scale as one GPU workload. It extracts features in a single fused on-chip pass, tiling long series so that the longest few do not become the bottleneck, and fits a cheaper ridge classification head. On 188 datasets spanning four archives (UCR, UEA, Bake Off Redux and Multiverse-core), with 279,247 training and 249,041 test series, FlashRocket trains and tests all 188 classifiers, from raw series to predicted labels, in 3.07 seconds on one L40S GPU with 16 CPUs. This is 242x faster than MiniRocket in tsai at matching accuracy (83.07% against 83.10%), and 298x faster and 1.34 points more accurate than MantisV2. FlashRocket makes it possible to retrain and test archive-scale collections of classifiers in seconds.