Forecourt Prism
A clearer view of demand
Inspect short-term volume baselines and the effect of missing observations.
Explore the evidence
Select a precomputed synthetic case, inspect input assumptions and download its JSON evidence or result CSV.
Open Forecourt Prism explorer →What this edition demonstrates
Weekday-shrunk trailing baseline with backtest errors.
Synthetic volumes and heuristic ranges; not operational demand predictions.
The public website hosts a read-only synthetic projection. The full authenticated source application and its Git history are maintained privately.
All fixtures are fictional. No sign-in, customer records, operational writes or paid external model calls are involved.
Publication notices · Application metadataExplore the application directory
Governing Equations, Inputs, Outputs & Dimensional Conventions
Forecourt Prism executes deterministic client-side calculations in the browser. Every scenario is governed by explicit mathematical formulations and unit checks:
Declared Units & Arithmetic Conventions: Fuel volume in litres/day (L/day); observation window in days (35 d); backtest window K = 21 days; forecast horizon H = 7 days.
Scope & Operational Boundary: Synthetic forecourt-volume forecasting with weekday-shrunk baselines and missing-data diagnostics. Not EV telemetry, live sales data or streaming analytics.
Launch Interactive Forecourt Prism Runtime → · Read Full Methodology & Validation Notes → · Download JSON Dataset (run/catalog.json)
Verified Case Study & Held-Out Reference Table
Worked Verification Case: Case 1 (35 observed days, synthetic baseline 600 L/day, zero missing days, 7-day forecast horizon): The weekday-shrunk trailing estimator computes a Daily Baseline of 603.4 L/day with a 21-point held-out Backtest MAE of 62.2 L/day. For 2026-09-05, the point forecast is 557.7 L with heuristic band [448.3 L, 667.0 L].
| Case ID | Observed Days | Missing Pattern | Daily Baseline (L) | Backtest Points | Backtest MAE (L) | Horizon (d) |
|---|---|---|---|---|---|---|
| Case 1 (Complete Series) | 35 | None (0%) | 603.4 | 21 | 62.2 | 7 |
| Case 2 (Every 5th Day Missing) | 28 | 20% periodic gaps | 598.1 | 17 | 74.8 | 7 |
| Case 3 (High Volume Site) | 35 | None (0%) | 1,206.8 | 21 | 124.4 | 14 |
How to Use Forecourt Prism in 3 Reproducible Steps
- Select a Baseline Scenario or Practice Module: Open the interactive browser runtime and choose one of the shipped synthetic presets or diagnostic modules to load its verified input parameters and unit declarations.
- Inspect Intermediate Traces & Constraints: Review the computed ledger, error diagnostics (MAE/RMSE/margin/balance checks), or bilingual explanations alongside the governing equations documented on mkdata.co.technology/methodology/.
- Export Portable Evidence (JSON / CSV): Export your scenario results, assumptions, or local study plan as a self-contained JSON or CSV file for offline verification and local re-import.
Methodological & Boundary FAQs
What mathematical model and units does Forecourt Prism use?
Forecourt Prism applies b_w = (1 − λ) × mean(V_{d ∈ weekday(w)}) + λ × mean(V_{all}); Backtest MAE = (1/K) Σ_{k=1..K} |V_k − b_{w(k)}|. Units: Fuel volume in litres/day (L/day); observation window in days (35 d); backtest window K = 21 days; forecast horizon H = 7 days.
What are the operational boundaries and limitations of Forecourt Prism?
Synthetic forecourt-volume forecasting with weekday-shrunk baselines and missing-data diagnostics. Not EV telemetry, live sales data or streaming analytics. All computations run deterministically in the browser on synthetic or precomputed demonstration fixtures.
How can I reproduce the benchmark results in Forecourt Prism?
Open the interactive browser workspace at ./run/, select Case 1 to inspect the worked result (Case 1 (35 observed days, synthetic baseline 600 L/day, zero missing days, 7-day forecast horizon): The weekday-shrunk trailing estimator computes a Daily Baseline of 603.4 L/day with a 21-point held-out Backtest MAE of 62.2 L/day. For 2026-09-05, the point forecast is 557.7 L with heuristic band [448.3 L, 667.0 L].), and export the JSON or CSV evidence ledger.
Engineering Attribution & Interconnected Research Hub
Authored and maintained by Mohammad Rezwan Khan, PhD (Aalborg University · BESS QA/QC & Power Systems Specialist). Canonical URL: https://mkdata.co.technology/ · Source revision: verified-rel · Runtime SHA-256 prefix: verified-sha256 · Repository provenance: https://github.com/mohammadrezwankhan/forecourt-prism (private source engine; public deterministic browser projection).
Application Metadata (app.json) · AI / LLM Discovery Guide (llms.txt) · Full Technical Corpus (llms-full.txt) · Semantic Knowledge Graph (knowledge-graph.jsonld)
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