Forecasting financial time series with ensemble learning

The forecasting of financial time series is a challenging problem that has been addressed by many researchers due to the possible profit. We provide an analysis of using classical time series method to create an ensemble of exponential smoothing and ARIMA to solve forecasting tasks of financial time series. The algorithm is tested on several financial time series of different behaviors. The experimental results show that it is possible to improve the performance by using the ensemble method for financial time series forecasting.