Finance
LYNX has been used in finance time-series analysis using dimensions such as stock prices, stock buyback levels, historical price movements, and various fundamental and technical analysis measures. One of the core use cases for symbolic regression is in future stock price calculations.
Clustering
LYNX has been used in clustering analysis. In a broad sense, if you have a need for categorization with some known and unknown dimensions, LYNX is a great tool for understanding how your inputs and outputs are potentially correlated.
Sporting event prediction
LYNX has been used in outcome predictions for various sporting events. In a data-rich environment such as sports metrics, you can discover patterns in sports outcomes, forecasted points scored, and various other outcomes that use known input dimensions.
Economic
Are you trying to predict GDP, money supply, or CPI figures before the official numbers come out? LYNX can help. Given the correct inputs, LYNX can be used to calculate the correct formula that uses your inputs to predict your desired output.
Variable Optimization
If you are looking for data correlation between your inputs and outputs, you can use LYNX to see which variables are selected to be used most often. With LYNX you can always see the top 10 closest algorithms to joining your inputs and outputs, so by aggregating the variable usage you are able to see which of your input dimensions most accurately
Binary Output
If your data environment requires a “yes/no” output, LYNX can be used for that as well. Using 1 or 0 for your output variable allows you to try numerous potential input variables to predict the binary output.