Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression

2026-03-11

AI Summary

This work proposes STemDist, the first dual-dimensional dataset distillation framework tailored for spatiotemporal forecasting. Existing methods typically compress only a single dimension—either temporal or spatial—limiting their efficiency in large-scale spatiotemporal sequence prediction. In contrast, STemDist jointly compresses both time and space dimensions by integrating cluster-level coarse-grained distillation with subset-level fine-grained optimization. This approach significantly reduces training overhead while simultaneously improving prediction accuracy. Extensive experiments on five real-world datasets demonstrate that STemDist achieves up to 6× faster training, 8× less memory consumption, and up to a 12% reduction in prediction error compared to state-of-the-art baselines.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Time-Series/Data Streams

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Spatio-temporal time series are widely used in real-world applications, including traffic prediction and weather forecasting. They are sequences of observations over extensive periods and multiple locations, naturally represented as multidimensional data. Forecasting is a central task in spatio-temporal analysis, and numerous deep learning methods have been developed to address it. However, as dataset sizes and model complexities continue to grow in practice, training deep learning models has become increasingly time- and resource-intensive. A promising solution to this challenge is dataset distillation, which synthesizes compact datasets that can effectively replace the original data for model training. Although successful in various domains, including time series analysis, existing dataset distillation methods compress only one dimension, making them less suitable for spatio-temporal datasets, where both spatial and temporal dimensions jointly contribute to the large data volume. To address this limitation, we propose STemDist, the first dataset distillation method specialized for spatio-temporal time series forecasting. A key idea of our solution is to compress both temporal and spatial dimensions in a balanced manner, reducing training time and memory. We further reduce the distillation cost by performing distillation at the cluster level rather than the individual location level, and we complement this coarse-grained approach with a subset-based granular distillation technique that enhances forecasting performance. On five real-world datasets, we show empirically that, compared to both general and time-series dataset distillation methods, datasets distilled by our STemDist method enable model training (1) faster (up to 6X) (2) more memory-efficient (up to 8X), and (3) more effective (with up to 12% lower prediction error).

Problem

• spatio-temporal forecasting
• dataset distillation
• bi-dimensional compression
• time series
• deep learning

Innovation

• spatio-temporal forecasting
• dataset distillation
• bi-dimensional compression
• cluster-level distillation
• time series