Predicting the highest and lowest stock price before end of the day - Choo, Zhi Cheng (2014)
In this paper, an ensemble model for forecasting highly complex financial time series is being introduced. To use the Autoregressive Integrated Moving Average (ARIMA) and Random Walk with Drift (RWDRIFT) models to capture the characteristics of highly complex financial time series. Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately. So ARIMA-RWDRIFT has shown better forecasts by taking advantage of each model’s capabilities.