用图神经网络分析时间序列，自动判断数据来自哪个地理位置。
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
- 构建双塔图神经网络，分别学习地理和时间特征。
- 在多个国家电力数据集上，定位准确率平均提升27%。
- 适合需要时空关联分析的智能电网、城市计算场景。
本文提出一种新型时间序列地理定位方法，旨在推断原始时间序列的地理来源。成功的地理定位可为时间序列提供空间上下文，支持下游位置感知应用。我们正式定义该问题，借鉴图像地理定位的核心思想建立强基线，并提出GeoGNN——一种双塔图神经网络架构。训练时，空间塔利用地理邻接图学习地理单元候选的嵌入表示，时间塔则从时间序列中提取信息特征；推理时，通过点积相似度将时间特征与候选地理嵌入匹配，并结合辅助分类头预测时间序列对应的地理起源。在大规模全国范围电力消费数据集上的实验表明，GeoGNN在多个数据集上表现最优，平均提升细粒度与粗粒度地理定位准确率约27%。
原文摘要 · Abstract (English)
This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Successful geolocalization can provide spatial context to time series, enabling downstream location-aware applications. We formalize the problem, adapt core ideas from image geolocalization to establish strong baselines, and propose GeoGNN, a two-tower architecture. During training, GeoGNN's spatial tower learns embeddings of geographic cell candidates by leveraging the geographic adjacency graph, while the temporal tower extracts informative representations from time series. During inference, each temporal representation is matched against candidate geographic embeddings using dot-product similarity, combined with an auxiliary classification head, to predict the time series' associated geographic origin. Experiments on large-scale, countrywide electricity-consumption datasets demonstrate that GeoGNN achieves the best performance across datasets and enhances both fine- and coarse-grained geolocalization accuracy by ~27% on average.
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