Our approach
Predictive models in government must be defensible. We apply them where there is enough data and a real decision to inform, such as shelter demand, staffing, grant expenditure, and training capacity, and we present the assumptions and uncertainty alongside the forecast.
Modeling services
Useful and defensible.
- Use-case assessment: Which decisions would benefit from forecasting and whether the data supports it.
- Model development: Statistical and machine learning models built for the decision, validated against history.
- Scenario analysis: What-if analysis for planning, budgeting, and exercises.
- Model monitoring: Tracking model performance over time and retraining when conditions change.
Applications
Where forecasting has earned its place.
- Capacity and demand: Shelter, staffing, and resource demand forecasts for planning and activation.
- Recovery and grants: Expenditure, timeline, and risk forecasts for recovery programs.
- Hazard and risk: Exposure modeling that feeds mitigation planning and risk scoring.
- Program planning: Training and exercise capacity planning against workforce turnover.
Why High Street?
High Street data scientists have built statistical and machine learning models for government and commercial clients, and we work with the practitioners who will act on the forecast, so the model answers a real question.