Forecasting: Demand & Energy Time-Series

At a glance

Task: regression  |  Headline: r2 = 0.9318

Summary

Project

Config-driven data science pipeline for high-fidelity industrial domains.

What was investigated

The engagement modelled the target 'demand_kw' as a regression problem in the Forecasting: Demand & Energy Time-Series domain, using 2000 records across 6 columns (data source: Synthetic sandbox). The pipeline audited data quality, engineered features, split the data honestly into train/test, and compared several models by cross-validation.

Outcome

The selected model was ExtraTrees, with r2 = 0.9318 on the held-out test set. Supporting metrics: rmse=193.166, mae=154.516, median_ae=137.289, mape=4.160, adj_r2=0.928, explained_variance=0.946, mean_residual=89.363. Full results, the deployment gates and the audit trail are in the run's report.

Figures

Artifacts

Data source: Synthetic sandbox

Audit SHA-256: 0dca8fead1f25ec5cd9aa899d97228f1d933ada2ccb2d64ba70520ab77cc7124