πŸ›’ USA Crude Oil Production Forecasting

πŸ” Goal:
Forecast global crude oil production to support strategic planning, pricing analysis, and energy resource
management.

πŸ“Š Dataset

  • Source: U.S. Energy Information Administration (EIA)

πŸ“ˆ Approach:
Utilized historical production data and applied time series forecasting methods (likely ARIMA or Holt-Winters)
to model trends and project future output levels.

🧰 Tools Used:
Python, Pandas, Numpy, Matplotlib, Statsmodels

πŸ“Š Key Insights:

  • Β Global crude oil production exhibits a seasonal and long-term trend, with fluctuations tied to
    geopolitical and economic cycles.
  • The model successfully captured historical volatility and projected a gradual production increase in
    the near term.
  • Anomalies in recent years (e.g., drops due to pandemics or market shocks) were accounted for in the
    model, ensuring robust forecasting.

βœ… Impact:

  • Enables stakeholders in energy economics and supply chain planning to anticipate production levels and
    adjust strategies accordingly.
  • The analysis helps identify potential future supply shortfalls or surpluses, allowing stakeholders to
    proactively manage associated market and economic risks.
  • Insights derived from the forecast can guide strategic investments in new infrastructure, technology, or
    alternative energy sources, leading to more efficient resource allocation.
  • This forecast provides crucial, data-driven input for government policies (e.g., export quotas, budget
    planning) and corporate strategies (e.g., capital expenditure on new drilling, mergers and acquisitions,
    diversification).

πŸ”— Project Repository