Spatio-temporal data, which commonly arise in real-world applications such as traffic monitoring, financial transactions, and ride-share demands, represent a special category of multivariate time series. They exhibit two distinct characteristics: high dimensionality and commensurability across spatial locations. These attributes call for computationally efficient modeling approaches and facilitate the use of univariate forecasting models in a channel-independent fashion. SparseTSF, a recently introduced competitive univariate forecasting model, harnesses periodicity to achieve compactness by concentrating on cross-period dynamics, thereby extending the Pareto frontier with respect to model size and predictive performance. Nonetheless, it underperforms on spatio-temporal data due to an inadequate capture of intra-period temporal dependencies. To address this shortcoming, we propose UltraSTF, which integrates a cross-period forecasting module with an ultra-compact shape bank component. Our model effectively detects recurring patterns in time series through the attention mechanism of the shape bank component, thereby strengthening its ability to learn intra-period dynamics. UltraSTF achieves state-of-the-art performance on the LargeST benchmark while employing fewer than 0.2% of the parameters required by the second-best approaches, thus further extending the Pareto frontier of existing methods.
View on arXiv@article{yeh2025_2502.20634,
title={ A Compact Model for Large-Scale Time Series Forecasting },
author={ Chin-Chia Michael Yeh and Xiran Fan and Zhimeng Jiang and Yujie Fan and Huiyuan Chen and Uday Singh Saini and Vivian Lai and Xin Dai and Junpeng Wang and Zhongfang Zhuang and Liang Wang and Yan Zheng },
journal={arXiv preprint arXiv:2502.20634},
year={ 2025 }
}