Synthetic case explorer

DemandLoom Studio

See demand ahead

Compare forecast horizons and demand scenarios on an original synthetic series.

Explore the evidence

Select a precomputed synthetic case, inspect input assumptions and download its JSON evidence or result CSV.

Open DemandLoom Studio explorer →

What this edition demonstrates

Chronological model selection, holdout diagnostics and empirical uncertainty bands.

Precomputed examples; future accuracy and band coverage are not guaranteed.

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.

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MATHEMATICAL SPECIFICATION & UNITS

Governing Equations, Inputs, Outputs & Dimensional Conventions

DemandLoom Studio executes deterministic client-side calculations in the browser. Every scenario is governed by explicit mathematical formulations and unit checks:

Declared Units & Arithmetic Conventions: Demand in hourly synthetic units; horizon H in hours (12 h to 48 h); scenario adjustment s in percent (%); error metrics in demand units and WAPE in %.

Scope & Operational Boundary: Synthetic demand forecasting with chronological model selection, holdout diagnostics and uncertainty bands. Precomputed browser demonstration; not live enterprise forecasting or investment modelling.

Launch Interactive DemandLoom Studio Runtime → · Read Full Methodology & Validation Notes → · Download JSON Dataset (run/catalog.json)

REPRODUCIBLE WORKED EXAMPLE & BENCHMARK TABLE

Verified Case Study & Held-Out Reference Table

Worked Verification Case: Case 1 (56-day synthetic history, seed=17, H=12 h horizon, -15% demand scenario): Rolling 3-fold validation selects Seasonal Persistence (selection MAE = 28.47) over Moving Average and Linear Trend. On the 12-hour holdout window, MAE = 25.10 units, RMSE = 33.82 units, Bias = -16.54 units, and WAPE = 1.97%.

Reproducible benchmark scenarios shipped in the DemandLoom Studio browser edition (Synthetic / Precomputed Evidence)
Model Candidate	Fold 1 MAE	Fold 2 MAE	Fold 3 MAE	Selection MAE	Holdout MAE (12h)	Holdout RMSE	Holdout WAPE (%)
Seasonal Persistence	31.11	29.29	24.99	28.47	25.10	33.82	1.97%
Trailing Moving Average	42.18	39.85	36.40	39.48	37.90	45.12	2.98%
Damped Linear Trend	35.60	33.14	29.80	32.85	30.45	38.90	2.39%

PROCEDURAL USAGE GUIDE

How to Use DemandLoom Studio 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 mkgrow.co.business/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.

FREQUENTLY ASKED QUESTIONS

Methodological & Boundary FAQs

What mathematical model and units does DemandLoom Studio use?

DemandLoom Studio applies MAE = (1/H) Σ |y_t − ŷ_t|; RMSE = sqrt((1/H) Σ (y_t − ŷ_t)²); WAPE (%) = (Σ |y_t − ŷ_t| / Σ |y_t|) × 100; Scenario Projection ŷ_scen(t) = ŷ_selected(t) × (1 + s/100). Units: Demand in hourly synthetic units; horizon H in hours (12 h to 48 h); scenario adjustment s in percent (%); error metrics in demand units and WAPE in %.

What are the operational boundaries and limitations of DemandLoom Studio?

Synthetic demand forecasting with chronological model selection, holdout diagnostics and uncertainty bands. Precomputed browser demonstration; not live enterprise forecasting or investment modelling. All computations run deterministically in the browser on synthetic or precomputed demonstration fixtures.

How can I reproduce the benchmark results in DemandLoom Studio?

Open the interactive browser workspace at ./run/, select Case 1 to inspect the worked result (Case 1 (56-day synthetic history, seed=17, H=12 h horizon, -15% demand scenario): Rolling 3-fold validation selects Seasonal Persistence (selection MAE = 28.47) over Moving Average and Linear Trend. On the 12-hour holdout window, MAE = 25.10 units, RMSE = 33.82 units, Bias = -16.54 units, and WAPE = 1.97%.), and export the JSON or CSV evidence ledger.

PROVENANCE, ATTRIBUTION & RESEARCH ECOSYSTEM

Engineering Attribution & Interconnected Research Hub

Authored and maintained by Mohammad Rezwan Khan, PhD (Aalborg University · BESS QA/QC & Power Systems Specialist). Canonical URL: https://mkgrow.co.business/ · Source revision: verified-rel · Runtime SHA-256 prefix: verified-sha256 · Repository provenance: https://github.com/mohammadrezwankhan/demandloom-studio (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)