Leakage-Aware Forecasting Workflow

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A structured workflow for time-series forecasting that incorporates chronological data partitioning, feature selection, and exogenous driver modeling under the Perfect Prognosis setting to prevent information leakage.

Why It Matters

This workflow specifically addresses data leakage in time-series forecasting by careful partitioning and integration of exogenous variables, a critical issue for model deployment.

Evidence

we developed a leakage-aware forecasting workflow that combined chronological data partitioning, preprocessing, feature selection, and exogenous-driver modeling under the Perfect Prognosis setting.

Related Papers

• openalex-2603.25495-interpretable-pm25-forecasting-for-urban-air-quality-a-compa