Towards an efficient machine learning model for financial time series forecasting

Financial time series forecasting is a challenging problem owing to the high degree of randomness and absence of residuals in time series data. Existing machine learning solutions normally do not perform well on such data. In this study, we propose an efficient machine learning model for financial time series forecasting through carefully designed feature extraction, elimination, and selection strategies. We leverage a binary particle swarm optimization algorithm to select the appropriate features and propose new evaluation metrics, i.e. mean weighted square error and mean weighted square ratio, for better performance assessment in handling financial time series data. Both indicators ascertain that our proposed model is effective, which outperforms several existing methods in benchmark studies.