SHARE PRICE FORECASTING THROUGH DATA MINING WITH COMBINATORY EVOLUTIONARY ALGORITHMS
Sereval researches have been carried out in order to identify an accurate and reliable share price forecast through simulation, time series analysis, combination of artificial intelligence and time series analysis methods and recently combination of data mining and artificial intelligence with evolutionary optimization methods and algorithms. In this research, through CRISP process of data mining and reviewing recent held reaserches on combinatory algorithms of forecasting share price, combination of artificial neural network and evolutionary optimization algorithms opted to forecast share price. Genetic algorithm (GA), particle swarm optimization algorithm (PSO) and imperialistic competition algorithm (ICA) used to train artificial neural network (ANN) with share price time series reducted data of five chosed trading symbols. Mean Squarred Error (mse) records demonstrate that ANN trained with particle swarm optimization algorithm results more accurate and reliable forecasts in comparison with other ANN trained with other noticed evolutionary optimization algorithms. Also, inefficiency of ANN trained with backpropagation algorithm on account of weak recognition of turbulent data is challenged as an adjunct outcome.
Key words: forecasting, data mining, artificial neural network, evolutionary optimization algorithms
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