Exploring Ceemdan Decomposition for Improved Financial Market Forecasting: A Case Study on Dow Jones Index

This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices. CEEMDAN adaptively decomposes complex financial time series into intrinsic mode functions (IMFs), a technique that has yet to be extensively utilized in this domain. IMFs are combined with ARIMAX's predictive proficiency, which accounts for the influence of historical trends and external factors. Our study showcases an R² of 0,93, aligning with some of the high-performing models in the literature. However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.