UltraSTF：面向大规模时空预测的超紧凑模型
UltraSTF: Ultra-Compact Model for Large-Scale Spatio-Temporal Forecasting
AI总结：
针对SparseTSF在时空数据上周期内依赖捕获不足的问题，提出集成跨周期预测与超紧凑形状库的UltraSTF模型，在LargeST基准上以少于次优方法0.2%的参数实现最先进性能。
摘要：
时空数据在交通监控、金融交易和拼车需求等现实应用中普遍存在，是高维多元时间序列的一种特例。这种高维性要求模型具备计算效率，并受益于通过通道独立策略应用单变量预测方法。近期提出的具有竞争力的单变量预测模型SparseTSF，利用周期性聚焦跨周期动态实现紧凑性，在模型规模和预测性能方面拓展了帕累托前沿。然而，由于对周期内时间依赖性的捕获有限，该模型在时空数据上表现欠佳。为解决这一局限，我们提出UltraSTF，它集成了跨周期预测组件与超紧凑形状库组件。我们的模型利用形状库组件的注意力机制高效捕获时间序列中的重复模式，显著增强其学习周期内动态的能力。UltraSTF在LargeST基准上取得了最先进的性能，同时使用的参数少于次优方法所需参数的0.2%，从而进一步拓展了现有方法的帕累托前沿。
英文摘要：
Spatio-temporal data, prevalent in real-world applications such as traffic monitoring, financial transactions, and ride-share demands, represents a specialized case of multivariate time series characterized by high dimensionality. This high dimensionality necessitates computationally efficient models and benefits from applying univariate forecasting approaches through channel-independent strategies. SparseTSF, a recently proposed competitive univariate forecasting model, leverages periodicity to achieve compactness by focusing on cross-period dynamics, extending the Pareto frontier in terms of model size and predictive performance. However, it underperforms on spatio-temporal data due to limited capture of intra-period temporal dependencies. To address this limitation, we propose UltraSTF, which integrates a cross-period forecasting component with an ultra-compact shape bank component. Our model efficiently captures recurring patterns in time series using the attention mechanism of the shape bank component, significantly enhancing its capability to learn intra-period dynamics. UltraSTF achieves state-of-the-art performance on the LargeST benchmark while utilizing fewer than 0.2% of the parameters required by the second-best methods, thereby further extending the Pareto frontier of existing approaches.
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