QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization
This work proposes QUALS, a large-scale time series corpus equilibrium framework that significantly enhances data efficiency, enabling existing models to achieve superior performance using only a small fraction of the original training data.
Yujie Li, Zezhi Shao, Cheng-Qing Yu et al.
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