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 themore »
Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting
Due to the potentially significant benefits for society, forecasting spatio-temporal societal events is currently attracting considerable attention from researchers. Beyond merely predicting the occurrence of future events, practitioners are now looking for information about specific subtypes of future events in order to allocate appropriate amounts and types of resources to manage such events and any associated social risks. However, forecasting event subtypes is far more complex than merely extending binary prediction to cover multiple classes, as 1) different locations require different models to handle their characteristic event subtype patterns due to spatial heterogeneity; 2) historically, many locations have only experienced a incomplete set of event subtypes, thus limiting the local model’s ability to predict previously “unseen” subtypes; and 3) the subtle discrepancy among different event subtypes requires more discriminative and profound representations of societal events. In order to address all these challenges concurrently, we propose a Spatial Incomplete Multi-task Deep leArning (SIMDA) framework that is capable of effectively forecasting the subtypes of future events. The new framework formulates spatial locations into tasks to handle spatial heterogeneity in event subtypes, and learns a joint deep representation of subtypes across tasks. Furthermore, based on the “first law of geography”, spatiallyclosed tasks share more »
- Publication Date:
- NSF-PAR ID:
- 10135598
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 33
- Page Range or eLocation-ID:
- 3638 to 3646
- ISSN:
- 2159-5399
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1609/aaai.v33i01.33013638
