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Predicting Spatio-Temporal Propagation of Seasonal Influenza Using Variational Gaussian Process Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add long-term squared-exponential kernel to periodic kernel (ablation) | GP time kernel · CDC 1972-2006 total US flu count | MSE: 0.0552 -> 0.0433 | Helped |
| add short-term non-stationary Paciorek kernel to periodic + long-term kernel (ablation) | GP time kernel · CDC 1972-2006 total US flu count | MSE: 0.0433 -> 0.0167 | Helped |
| variational GP with combined kernels instead of GP subset of data | flu prediction · GFT 1-week-ahead | MSE: 0.0156 -> 0.0043 | Helped |
| variational GP instead of k-NN regression | flu prediction · GFT 1-year-ahead | MSE: 0.3586 -> 0.2900 | Helped |
| variational GP instead of k-NN regression | flu prediction · GFT 2-weeks-ahead | MSE: 0.0105 -> 0.0128 | Hurt |
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