A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction
Document Type
Article
Publication Date
8-1-2023
Abstract
In 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.
Keywords
Online learning, Multivariate time series, Multi-task learning, COVID-19 prediction
Divisions
fsktm
Funders
King Mongkut's Institute of Technology Ladkrabang Research Fund [Grant No: KREF206306]
Publication Title
Heliyon
Volume
9
Issue
8
Publisher
Cell Press
Publisher Location
50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA