A WNN-CSO model for accurate forecasting of chaotic and nonlinear time series

Accurate forecasting of chaotic and nonlinear time series has been a key area of research in last two decades. They find extensive applications in stock market prediction, forecasting weather conditions, determining the inferences of chemical reactions and many more. The manuscript deals with development of a new hybrid model based on Wavelet Neural Network (WNN) trained by Cat Swarm Optimization (CSO). The performance of the proposed model is accessed on benchmark time series like `Mackey-Glass´ and `Box Jenkins´. Comparison with WNN-PSO, Chebyshev FLANN and MLP-BP models reveal the superior performance of the proposed model in terms of response matching, Minimum MSE and lower SSE values achieved. Therefore the WNN-CSO model is a preferred candidate for accurate prediction of Chaotic and Nonlinear time series.