Time series prediction with a hybrid system formed by articial neural network and cognitive development optimization algorithm

Time series prediction is a remarkable research interest that is widely followed by scientists and researchers. Because many elds include processes of such time series analyses, dierent kinds of approaches, methods, and techniques are employed often in order to achieve alternative ways of prediction. It appears that articial-intelligence-based solutions have strong potential for providing eective and accurate prediction approaches in even most complicated time series structures. For further details and explanation, this study aims to introduce an alternative articial-intelligence-based approach to articial neural networks and cognitive development optimization algorithm, as a recent intelligent optimization technique introduced by the authors. This study aims to predict dierent kinds of time series by using the introduced system/approach. In this way, it is possible to discuss application potential of the hybrid system and report ndings related to its success of prediction. The authors believe that the study provides a good chance to support the literature with an alternative solution approach and see the potential of a newly developed, articial-intelligence-based optimization algorithm for dierent applications.