Full distributions
Produces calibrated temperature probabilities at 0.1°C resolution for horizons from D+0 to D+4, preserving uncertainty rather than collapsing it into one forecast.
All work
Advanced Model
ForecastBot
An advanced model that fuses diverse weather feeds into calibrated probability distributions for daily maximum temperature.
21
upstream weather feeds
0.1°C
distribution resolution
D+0-D+4
forecast horizons
Overview
ForecastBot is a probabilistic forecasting engine for prediction markets where weather is the underlying event. It does not run an atmospheric model of its own. Instead, it ingests deterministic and ensemble data from diverse upstream weather feeds, calibrates their behaviour and combines them into a complete probability distribution for daily maximum temperature, allowing consumers to work with quantiles, confidence and tail risk rather than one point estimate.
Product capabilities
Built around the work that matters.
Multi-source fusion
Combines 21 upstream weather and model feeds through source-specific calibration and adaptive probabilistic weighting informed by a rolling accuracy ledger.
Live forensics
Tracks source health, model agreement and forecast misses so unusual behaviour can be inspected, replayed and turned into structured model feedback.
Technical approach
A strictly typed asynchronous Python runtime coordinates long-lived source pollers and an event-driven probabilistic pipeline. FastAPI and WebSockets serve live distributions to a React dashboard, while PostgreSQL with TimescaleDB stores time-series history. Statistical modelling combines gradient-boosted calibration, Bayesian model averaging and a guarded challenger model, with Prometheus and Grafana covering operational visibility.
Engineering highlights
Complexity, handled deliberately.
Adaptive blending
Probabilistic source weights respond to changing error patterns, while a challenger model is evaluated against rolling forecast scores before it can replace the serving approach.
Forensic resilience
Append-only recovery data, snapshots and structured forecast captures support restart and investigation, including counterfactual replays when a prediction misses an important threshold.
Product gallery
A closer look.
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