Abstract
In many bio-surveillance and healthcare applications, data sources are measured from many spatial locations repeatedly over time, say, daily/weekly/monthly. In these applications, we are typically interested in detecting hot-spots, which are defined as some structured outliers that are sparse over the spatial domain but persistent over time. In this paper, we propose a tensor decomposition method to detect when and where the hot-spots occur. Our proposed methods represent the observed raw data as a three-dimensional tensor including a circular time dimension for daily/weekly/monthly patterns, and then decompose the tensor into three components: smooth global trend, local hot-spots, and residuals. A combination of LASSO and fused LASSO is used to estimate the model parameters, and a CUSUM procedure is applied to detect when and where the hot-spots might occur. The usefulness of our proposed methodology is validated through numerical simulation and a real-world dataset in the weekly number of gonorrhea cases from 2006 to 2018 for 50 states in the United States.
Original language | English (US) |
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Pages | 289-310 |
Number of pages | 22 |
State | Published - 2020 |
Event | 13th International Workshop on Intelligent Statistical Quality Control 2019, IWISQC 2019 - Hong Kong, Hong Kong Duration: Aug 12 2019 → Aug 14 2019 |
Conference
Conference | 13th International Workshop on Intelligent Statistical Quality Control 2019, IWISQC 2019 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 8/12/19 → 8/14/19 |
Keywords
- Circular time
- CUSUM
- Hot-spot
- Spatio-temporal model
- Tensor decomposition
ASJC Scopus subject areas
- Computer Networks and Communications