I experimented with using geometric analysis to compare past data such as weather, commodity prices, and conditions to predict future prices. My hypothesis was that by employing geometric analysis techniques on historical data, I could identify patterns and correlations that would enable me to make accurate predictions about future prices. 

I conducted geometric analysis on past data sets related to weather, commodity prices, and various conditions to identify patterns and trends. Geometric sequencing methods were employed to analyze historical data sets, looking for recurring shapes, trends, and relationships. Testing involved comparing the predictions generated by the geometric analysis with actual future prices to evaluate the accuracy and reliability of the approach.

The conclusion drawn from the experiment was that while geometric analysis showed potential in predicting future prices based on past data, its effectiveness varied depending on the complexity and volatility of the analyzed factors. Additionally, other external variables not accounted for in the analysis could significantly impact the accuracy of predictions. The experiment came close to the initial hypothesis by demonstrating the potential of geometric analysis in predicting future prices based on past data. However, further refinement and validation are required to improve the accuracy and reliability of predictions in real-world scenarios.

To further enhance the predictive capabilities of the approach, I could explore incorporating additional data sources, refining geometric analysis techniques, and considering advanced statistical methods.