Centers for Disease Control and Prevention • Center for Forecasting and Outbreak Analytics
Automated Gaussian Process model discovery for time series data with significant on-going revisions
NowcastAutoGP.jl is a Julia package for combining nowcasting of epidemiological time series data with forecasting using an ensemble of Gaussian process (GP) models. The package was developed for the CDC Center for Forecasting and Outbreak Analytics (CFA) to support real-time situational awareness and epidemiological forecasting.
The basic idea is to use the incremental fitting capabilities of AutoGP.jl to batch forecasts over probabilistic nowcasts of recent data points. This accounts for uncertainty in recent data points that are still being revised, while leveraging the flexibility and scalability of Gaussian processes for forecasting.
- Nowcasting integration: Handles data revision uncertainty in recent time periods
- Flexible approach: Agnostic to the nowcasting method used
- Ensemble forecasting: Uses Gaussian process model discovery for robust predictions
- Real-time capable: Designed for operational epidemiological surveillance
using Pkg
Pkg.add(url="https://github.com/CDCgov/NowcastAutoGP.jl")NowcastAutoGP.jl allows the user to incorporate nowcasting with ensemble Gaussian process (GP) forecasting provided by AutoGP.jl. In the example below, we show forecasting with the "naive" belief that the most recent reported data is accurate and final, compared to forecasting that incorporates simple nowcasting that accounts for a reporting multiplicative factor based on historical reporting patterns.
Naive forecasting consistently underestimates due to reporting delays
Forecasts incorporating simple nowcasting show improved accuracy
Score ratios demonstrate clear performance improvements with nowcasting
- Getting Started Tutorial: Complete example using NHSN COVID-19 hospitalization data
- API Reference: Detailed function documentation
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