Enhancing Stock Price Prediction Using Empirical Mode Decomposition, Rolling Forecast and Combining Statistical Methods

Abstract

Data analytics especially predictive analytics is very important research domain which includes time series forecasting. Nonlinear nonstationary time series are challenging to predict. This paper presents the outcome of the research study in finding better forecasting methods for nonlinear nonstationary time series. Rolling forecast approach and locally adaptive empirical mode decomposition (EMD)-based hybridization were employed with autoregressive integrated moving average (ARIMA) and exponentially weighted moving average (EWMA). Thus, two methods were EMD-ARIMArolling and EMD-EWMArolling of which the later was found better in this study. Also, EMD-EWMArolling was combined with ARIMArolling and EWMArolling using affine combinations to develop affEEArolling and affEEErolling methods. Proposed affEEArolling and affEEErolling along with six other compared methods were employed on nine closing price stock datasets from NASDAQ Financial-100 companies and compared using accuracy measurements. From the results, it was found that proposed methods significantly improved forecast accuracy and outperformed the compared methods (e.g., in ACGL dataset, affEEArolling reduced RMSFE by 55.98% where rolling forecast, EMD-hybridization and affine combination improved 43.7%, 4.24% and 18.28% respectively and affEEErolling improved 56%). Hence, EMD-based hybridizations and forecast combinations can be useful tools for time series forecasting. In addition, such EMD-based advanced methods can be considered for inclusion in financial technologies.