An evolutionary approach to time series forecasting with artificial neural networks
Carl Sandström · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2015
In this paper an evolutionary approach to forecasting the stock market is tested and compared with backpropagation. An neuroevolutionary algorithm is implemented and backtested measuring returns and the normalized-mean-square-error for each algorithm on selected stocks from NASDAQ. The results are not entirely conclusive and further investigation would be needed to say definitely, but it seems as a neuroevolutionary approach could outperform backpropagation for time series prediction.