Machine learning algorithms for time series in financial markets

This research focuses on the usefulness of various intelligent machine learning algorithms on prediction of time series in financial markets. A challenge in this area is that economic managers and the scientific community are still demanding predictive algorithms with greater accuracy. The elimination of the mentioned challenge can improve the quality of the predictions and, as a result, lead to higher profitability and productivity. The proposed solution relies on finding the best input variables by using the regression-based machine learning algorithms, with emphasis on the leading selection methods. We implemented the concerned ideas using the Python language and the relevant machine learning tools. In our experiments, as dataset, we used the stock information of two companies from the Tehran Stock Exchange. These datasets belong to the transactions accomplished in years 2008 to 2018. The experimental results show that the technical features selected by the forward method can find the most effective and also the best values for the required parameters. The experimental results and formal analyses indicate that the use of selected technical features as inputs to the support-vector-machine and to the multi-layer perceptron machine gives prediction with the least-error, and this would provide more accurate predictions