Time series forecasting
Demand, cost, and capacity forecasts with proper temporal cross-validation, not random splits.
AI & Machine Learning
Predictive Modelling & Forecasting
Forecasting, pricing, and anomaly detection models built on your operational history, with the validation work that tells you whether to trust them.
Overview
Prediction problems fail quietly. A model reports strong offline metrics, goes live, and nobody notices it has been wrong for a month. We spend as much effort on backtesting and monitoring as on the model itself.
Typical work includes price and cost prediction, demand forecasting, anomaly detection over transactional data, and validation layers that check a predicted figure against rules before it reaches a customer.
What you receive
- Model with backtest results across multiple time windows
- Feature pipeline reproducible from raw source data
- Validation rules for implausible predictions
- Dashboard tracking live error against baseline
- Documented assumptions and known failure modes
Typical stack
- Python
- scikit-learn
- XGBoost
- Prophet
- Pandas
- PostgreSQL
Capabilities
What this covers in practice
Price and cost prediction
Estimation models for quoted figures, paired with a rules layer that catches implausible outputs.
Anomaly and exception detection
Surfacing the small fraction of records that need a human, so review effort goes where it pays.
Backtesting and monitoring
Replaying the model against history, then tracking live error so degradation is visible early.
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Read moreWant to talk through a predictive modelling & forecasting project?
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