Vantage — Forecasting & Predictive Analytics
A dependency-free forecasting engine that shows its error before it shows its forecast — the discipline a regulated organisation should demand of any number it plans on.
01The problem
Organisations plan against demand they cannot see — stock, staffing, budgets, capacity — and the forecasts they plan on usually arrive as unexplained point estimates: no error history, no uncertainty band, no evidence the model beats a naive baseline on data it has never seen. In a regulated or funded environment, that is not a modelling quibble; it is a governance failure. A budget submitted to a board or funder, a capacity plan behind a service obligation, a threshold inside a monitoring system — each is a quantitative claim someone will eventually have to defend. The honest control question is always the same: how wrong has this method been, measured fairly, and does it beat the simple alternative? Most forecasting tools never make you ask it.
02What I built
Vantage is a self-contained, dependency-free forecasting engine wrapped in five industry skins — brewery, direct-to-consumer, education, manufacturing and SaaS — plus a free sandbox for your own numbers.
- The engine implements Holt-Winters, ETS, Croston (for intermittent demand), seasonal-naive and OLS regression, and selects the model per series by the lowest back-tested error on rolling origins — ranked on mean absolute error, with RMSE breaking ties (the same order as WAPE wherever WAPE is defined, and WAPE is the score shown), with sMAPE and bias reported alongside, all evaluated on data the model was not fitted to.
- Every forecast ships with 80% and 95% prediction intervals, widened with the horizon by a stated heuristic factor (stock plans use the 95% band), and horizons are planned in operationally sensible weekly or monthly steps.
- The embedded version is the live app: the engine's real forecasts, charts and intervals are computed in the browser on a locally-vendored React 18 runtime, so it runs fully offline.
03A lawyer's scepticism about expert numbers
Vantage encodes a lawyer's scepticism about expert numbers. Rolling-origin back-testing is the fair trial: a model is judged only on observations it had not seen, repeatedly, from multiple starting points — the quantitative equivalent of testing a witness on facts they were not coached on. The seasonal-naive baseline keeps every claim falsifiable: if a sophisticated method cannot beat “same as last season”, the engine says so and uses the simpler model — accuracy is earned, never presumed. And prediction intervals replace false precision: a decision-maker is given a range, and the rule that sets it, not a single confident number. This is the same posture a model-validation function, an auditor or a cross-examining counsel takes towards any quantitative evidence — documented method, measured error, honest uncertainty — built into the tool instead of bolted on after the challenge arrives.
04Where this lands in a compliance function
Compliance functions run on models — transaction-monitoring thresholds, risk scores, screening calibrations — and much of the advisory work around them is deciding whether a number can be relied on and evidenced. Because I have built and back-tested an engine of my own, I know which questions actually bite when a vendor makes an accuracy claim — out-of-sample error, baseline comparison, bias, drift — and I can document model choices in a way a validator or supervisor can follow, then translate the output into decisions a board can defend. Vantage is the numeracy layer of the portfolio: proof the analytical judgement runs deeper than the legal vocabulary.