Neuromorphic Inference Lab Neuromorphic Inference Lab

System

Forecast Studio

Forecasting treated as engineering: reproducible data pipelines, backtesting harness, versioned artefacts, and deployment-ready outputs. Designed to evidence full-stack ML (Data → Model → Production).

Time-Series Feature Pipelines Backtesting Model Registry CI/CD for ML
Source repository Proof ledger mapping Back to Systems

What problem it solves

Many “forecasting projects” stop at a notebook. Forecast Studio makes forecasting operational: stable feature generation, defensible evaluation, and outputs that can be served or scheduled.

Business impact

  • Forecasts generated from a repeatable pipeline (no manual spreadsheet steps).
  • Backtesting with consistent splits (prevents accidental leakage).
  • Deployment-ready output format for downstream planning tools.

System architecture

  • Ingest: batch data sources (CSV/DB extracts) → validated schema.
  • Transform: feature pipeline (lags, rolling stats, calendars).
  • Train: model selection + tuned baseline.
  • Evaluate: backtesting harness with metrics (MAE/RMSE/MAPE).
  • Publish: versioned artefacts + deployment output.

Interactive mini-demo (local, no backend)

This is a lightweight in-browser illustration: select a horizon and compare a baseline forecast to the last observed trend. It exists to show system thinking, not to replace the production pipeline.

Output
Horizon: 12 · Baseline: Naïve · MAE (demo split): —
Series preview
Solid = observed · Dashed = forecast (demo)
Demo data (generated) 

        

      

    


    

      
Keywords (ATS trigger set)

      

        

          

            Time-Series Forecasting
            Feature Engineering
            Backtesting
            Data Validation
            CI/CD for ML
            Model Registry
            Artifact Versioning
            Monitoring-ready Outputs
          

          

            Evidence mapping lives in the Proof Ledger so each keyword remains clickable and verifiable.