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- W98747420 abstract "The problem of forecasting in various technical, economic, and other systems is an important problem of nowadays. The methods of artificial intelligence and machine learning analyze very effectively various data including financial ones. The main problem of such techniques is the choice of model structure and the configuration of its parameters. In this paper we propose an evolutionary method for the neural network designing that does not require any expert knowl-edge in the area of neural networks and optimization theory from the user. This algorithm has been applied to the FOREX forecasting task of 13 different currency pairs based on the historical data for 12,5 years. The performance of the proposed algorithm has been compared to the forecasting results of other 6 algorithms. The proposed algorithm has shown the best performance on more than half of the tasks. On remaining tasks the algorithm yields slightly to the multi-layer perceptron trained by the particle swarm optimization algorithm. However, the predominance of the pro-posed algorithm is more significant. Keywords: neural networks, evolutionary algorithms, particle swarm optimization, FOREX forecasting." @default.
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- W98747420 date "2012-01-01" @default.
- W98747420 modified "2023-09-22" @default.
- W98747420 title "Evolutionary design of neural networks for forecasting of financial time series" @default.
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