QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization
QUALS improves zero-shot time-series foundation model training via pattern quantization and learnability synchronization, achieving better accuracy with a fraction of the data.
QUALS is a large-scale corpus equilibrium framework for time-series foundation models targeting zero-shot forecasting in domains like transportation and power grids. It combines a pattern quantization mechanism using vector quantization and uniform binning with a learnability synchronization framework that calibrates sampling weights between simple and complex motifs. Benchmarks show pre-training on QUALS delivers superior zero-shot performance even with substantially reduced training budgets.
- Uses vector quantization and uniform binning to decode heterogeneous patterns
- Calibrates sampling weights to bridge optimization gaps across motifs
- Achieves superior zero-shot forecasting with a small fraction of training data
From the original article
1 min read at arxiv.orgUbiquitous time series data across diverse domains enables critical applications in areas such as transportation systems and power grids. Recently, training foundation models on massive datasets to achieve accurate zero-shot forecasting has emerged as a major research focus.
By Yujie Li · arxiv.org
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