Forecasting System

Context: Global commodity pricing platform where the editorial team needed to move from reactive reporting to proactive, data-driven market intelligence.

Challenge: Editors needed reliable price forecasts to support strategic decisions across 12+ commodity categories (crude oil, LPG, coal, agriculture, chemicals, metals), but had no predictive infrastructure.

Solution: Built a time‑series forecasting system using:

• Regression and statistical modeling to project future price scenarios
• Interactive visualizations for possibility curves with confidence levels at different prediction horizons
• Percentage-based confidence scores to translate complex statistical outputs into intuitive decision‑support tools

Outcome: Enabled the 600+ editorial team to make proactive, data‑backed market calls, significantly improving the strategic value of their price reporting.