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The authors of this work performed exhaustive experiments in order to support the validity and efficacy of the Proposed Method. Nevertheless, the method should keep being validated and improved. Regarding the validation of the method, more experiments can be performed with other datasets and more comparisons with other works can be done. Regarding the improvement of the method, there are many areas of the algorithm that are known that can be improved, like applying dynamic adaptation of parameters to other parameters of the algorithm, such as the crossover, migration, and mutation chance. Also, the operators of the Genetic Programming algorithm can be modified to different sets in order to obtain better performing Membership Functions, and the ranges of the literals can be adjusted (or even dynamically adapted) to improve the overall performance of the algorithm.
In conclusion, one can see that the results are very satisfactory, and that the system can effectively be used as a Decision Support System, and as a technique to create regression models of financial time-series.