A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction

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dc.contributor.authorWu, Zipeng
dc.contributor.authorLoo, Chu Kiong
dc.contributor.authorObaidellah, Unaizah
dc.contributor.authorPasupa, Kitsuchart
dc.date.accessioned2026-08-06T10:42:01Z
dc.date.available2026-08-06T10:42:01Z
dc.date.issued2023-08-01
dc.description.abstractIn light of the ongoing COVID-19 pandemic, predicting its trend would significantly impact decision-making. However, this is not a straightforward task due to three main difficulties: temporal autocorrelation, spatial dependency, and concept drift caused by virus mutations and lockdown policies. Although machine learning has been extensively used in related work, no previous research has successfully addressed all three challenges simultaneously. To overcome this challenge, we developed a novel online multi-task regression algorithm that incorporates a chain structure to capture spatial dependency, the ADWIN drift detector to adapt to concept drift, and the lag time series feature to capture temporal autocorrelation. We conducted several comparative experiments based on the number of daily confirmed cases in 20 areas in California and affiliated cities. The results from our experiments demonstrate that our proposed model is superior in adapting to concept drift in COVID-19 data and capturing spatial dependencies across various regions. This leads to a significant improvement in prediction accuracy when compared to existing state-of-the-art batch machine learning methods, such as N-Beats, DeepAR, TCN, and LSTM.
dc.identifier.citationHeliyon, 9(8), 2023
dc.identifier.doi10.1016/j.heliyon.2023.e18771
dc.identifier.issn24058440
dc.identifier.other2-s2.0-85168378562
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14561
dc.sourceHeliyon
dc.subjectCOVID-19 prediction
dc.subjectMulti-task learning
dc.subjectMultivariate time series
dc.subjectOnline learning
dc.titleA novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction
dc.typeArticle

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