Commodity Forecasting

Problem Statement:

The challenge lies in forecasting commodity prices in a market characterized by high volatility. Traditional models often fail to account for sudden market changes, leading to inaccurate predictions.


We constructed a multivariate model that utilized data from various outsourced sources. By integrating diverse data points, the model was capable of comprehending the multifaceted nature of commodity markets. The results exhibited a 93% accuracy rate in general market conditions (8-12% volatility). Remarkably, even during periods of heightened volatility (up to 21%), the model maintained an accuracy of approximately 88%.


  • Dynamic Dashboard: Real-time visualization of forecasts with historical data comparison.
  • Adaptive Model: Adjusts to volatile market conditions to provide resilient accuracy.
  • Data Integration: Capability to integrate and process data from diverse outsourced sources.

Use Cases:

  • Commodity Traders: Enables traders to anticipate market moves and adjust their strategies accordingly.
  • Financial Analysts: Provides a tool to better understand market dynamics and prepare more accurate financial forecasts.
  • Supply Chain Managers: Assists in better procurement planning by anticipating price changes.

Data Science Specific Points:

  • Data Collection: Data was aggregated from multiple outsourced vendors. This includes historical price data, global economic indicators, and other relevant factors influencing the commodity market.
  • Data Analysis: We utilized a combination of time series analysis, deep learning algorithms, and traditional regression techniques to make sense of the diverse data.
  • Results: Our multivariate model achieved an impressive 93% accuracy in general market conditions and maintained an 88% accuracy even in extreme volatility conditions.

Technologies Used:

  • Python (for data processing and modeling)
  • TensorFlow & Keras (for deep learning)
  • Tableau (for dashboard visualization)
  • Scikit-learn (for traditional machine learning models)
  • SQL (for data storage and retrieval)
  • Refinitive, (for external data sourcing)