Modeling and forecasting economic growth by analyzing nonlinear time series

Document Type: Research Paper


1 Master student industrial Engineering ,University of sistan and Baluchestan

2 null

3 Assistant Professor, industrial Management, University of Imam khomeini


Correct prediction of economic growth in the long-term policy planning and sustainable development, plays an important role. One of the major issues in the forecast time series is methods to identify patterns to control the complexity and Optimization of forecasting error. In this study, non-linear time series analysis GDP to forecast the economic growth path using , bayesian neural networks, for greater flexibility of the Nonlinear model in dealing with the complexities and more compatible with the actual conditions. Then, we use combination of genetic meta-heuristic algorithms to improve efficiency in network training model results and it is compared to older discussed methods. Model used 1992 to 1992 datas to estimation and tested 2014 to first two seasons of 2016 datas using the SSE and MSE. Results show that the complexity of the reform in the education network will have an important role in the deduction of optimization error.


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