The forecasting of significant societal events such as civil unrest and economic crisis is an interesting and challenging problem which requires both timeliness, precision, and comprehensiveness. Significant societal events are influenced and indicated jointly by multiple aspects of a society, including its economics, politics, and culture. Traditional forecasting methods based on a single data source find it hard to cover all these aspects comprehensively, thus limiting model performance. Multi-source event forecasting has proven promising but still suffers from several challenges, including (1) geographical hierarchies in multi-source data features, (2) hierarchical missing values, (3) characterization of structured feature sparsity, and (4) difficulty in model’s online update with incomplete multiple sources. This article proposes a novel feature learning model that concurrently addresses all the above challenges. Specifically, given multi-source data from different geographical levels, we design a new forecasting model by characterizing the lower-level features’ dependence on higher-level features. To handle the correlations amidst structured feature sets and deal with missing values among the coupled features, we propose a novel feature learning model based on an N th-order strong hierarchy and fused-overlapping group Lasso. An efficient algorithm is developed to optimize model parameters and ensure global optima. More importantly, to enable the model update in real time, the online learning algorithm is formulated and active set techniques are leveraged to resolve the crucial challenge when new patterns of missing features appear in real time. Extensive experiments on 10 datasets in different domains demonstrate the effectiveness and efficiency of the proposed models.

中文翻译：

---

使用增量多源特征学习的时空事件预测

对内乱和经济危机等重大社会事件的预测是一个有趣且具有挑战性的问题，需要及时性、准确性和全面性。重大的社会事件受到社会的多个方面的共同影响和指示，包括其经济、政治和文化。基于单一数据源的传统预测方法难以全面涵盖所有这些方面，从而限制了模型性能。多源事件预测已被证明是有前景的，但仍面临一些挑战，包括 (1) 多源数据特征中的地理层次结构，(2) 层次缺失值，(3) 结构化特征稀疏性的表征，以及 (4)模型的在线更新，不完整的多个来源。本文提出了一种新颖的特征学习模型，可以同时解决上述所有挑战。具体来说，给定来自不同地理层次的多源数据，我们通过描述低层次特征对高层次特征的依赖性来设计一个新的预测模型。为了处理结构化特征集之间的相关性并处理耦合特征之间的缺失值，我们提出了一种基于 ñ th 阶强层次结构和融合重叠群 Lasso。开发了一种有效的算法来优化模型参数并确保全局最优。更重要的是，为了实现模型的实时更新，制定了在线学习算法，并利用主动集技术来解决实时出现新的缺失特征模式时的关键挑战。在不同领域的 10 个数据集上进行的大量实验证明了所提出模型的有效性和效率。