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Forecasting System

600+ Editorial Team12 Commodity Categories

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:

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

Tech Stack:

RRegressionStatistical ModelingPlotlyR Shiny