Liang Zhao, Jieping Ye, Feng Chen, Naren Ramakrishnan

Abstract

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 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.

People

Feng Chen


Liang Zhao


Naren Ramakrishnan


Publication Details

Date of publication:
September 13, 2021
Journal:
ACM Transactions on Knowledge Discovery from Data (TKDD)
Page number(s):
1-28
Volume:
16
Issue Number:
2
Publication note:

Liang Zhao, Yuyang Gao, Jieping Ye, Feng Chen, Yanfang Ye, Chang-Tien Lu, Naren Ramakrishnan: Spatio-Temporal Event Forecasting Using Incremental Multi-Source Feature Learning. ACM Trans. Knowl. Discov. Data 16(2): 40:1-40:28 (2022)