Stock Index Forecasting Using Time Series Decomposition-Based and Machine Learning Models

Abstract

Forecasting of financial time series is challenging due to its non-linear and non-stationary characteristics. Due to limitations of traditional time series models, it is difficult to forecast financial time series such as stock price and stock index. Hence, we used ensemble of time series decomposition-based models (such as Discrete Wavelet Transform, Empirical Mode Decomposition and Variational Mode Decomposition) and machine learning models (such as Artificial Neural Network and Support Vector Regression) for forecasting the close price of 25 major stock indices for a period of 10 years ranging from January 1, 2009 to December 31, 2018. Decomposition models are used to disaggregate the time series into various subseries and machine learning models are used for forecasting each subseries. The forecasted subseries are then aggregated to obtain the final forecast. The performance of the models was evaluated using Root Mean Square Error and was validated statistically using Wilcoxon Signed Rank Test. We found that the performance of ensemble models better than traditional machine learning models.