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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.03566 |
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| _version_ | 1866914073006833664 |
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| author | Prabu, Ashwin Tran, Nhat Thanh Zhou, Guofa Xin, Jack |
| author_facet | Prabu, Ashwin Tran, Nhat Thanh Zhou, Guofa Xin, Jack |
| contents | A variety of models have been developed to forecast dengue cases to date. However, it remains a challenge to predict major dengue outbreaks that need timely public warnings the most. In this paper, we introduce CrossLag, an environmentally informed attention that allows for the incorporation of lagging endogenous signals behind the significant events in the exogenous data into the architecture of the transformer at low parameter counts. Outbreaks typically lag behind major changes in climate and oceanic anomalies. We use TimeXer, a recent general-purpose transformer distinguishing exogenous-endogenous inputs, as the baseline for this study. Our proposed model outperforms TimeXer by a considerable margin in detecting and predicting major outbreaks in Singapore dengue data over a 24-week prediction window. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03566 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CrossLag: Predicting Major Dengue Outbreaks with a Domain Knowledge Informed Transformer Prabu, Ashwin Tran, Nhat Thanh Zhou, Guofa Xin, Jack Machine Learning Computers and Society A variety of models have been developed to forecast dengue cases to date. However, it remains a challenge to predict major dengue outbreaks that need timely public warnings the most. In this paper, we introduce CrossLag, an environmentally informed attention that allows for the incorporation of lagging endogenous signals behind the significant events in the exogenous data into the architecture of the transformer at low parameter counts. Outbreaks typically lag behind major changes in climate and oceanic anomalies. We use TimeXer, a recent general-purpose transformer distinguishing exogenous-endogenous inputs, as the baseline for this study. Our proposed model outperforms TimeXer by a considerable margin in detecting and predicting major outbreaks in Singapore dengue data over a 24-week prediction window. |
| title | CrossLag: Predicting Major Dengue Outbreaks with a Domain Knowledge Informed Transformer |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2510.03566 |