STL-ELM: A computationally efficient hybrid approach for predicting high volatility stock market

Abstract

• Hybrid STL-ELM Model: Combines STL decomposition with ELM for accurate stock market prediction. • Outperforms traditional machine learning, hybrid and attention-based models. • Computationally Efficient: Offers low runtime and memory usage, ideal for real-time trading. • Multiscale Decomposition: STL extracts trend, seasonal, and residual components for enhanced prediction. • Provides actionable insights for investors and policymakers in dynamic markets. Accurate forecasting of high-volatility stock markets is critical for investors and policymakers, yet existing models struggle with computational inefficiency and noise sensitivity. This study introduces STL-ELM, a novel hybrid model combining Seasonal-Trend decomposition using LOESS (STL) and Extreme Learning Machine (ELM), to deliver unparalleled accuracy and speed. By decomposing stock data into trend, seasonal, and residual components, STL-ELM isolates multiscale features, while ELM’s lightweight architecture ensures rapid training and robust generalization, outperforming advanced techniques such as LSTM, GRU, and transformer variants in both prediction and trading simulations. With faster runtimes and minimal memory usage, STL-ELM is tailored for real-time trading applications and high-frequency financial forecasting, offering institutional investors, traders, and financial analysts a competitive edge in volatile markets. The hybrid nature of STL-ELM, which combines STL’s multiscale decomposition with ELM’s rapid learning, enhances its adaptability to various financial domains, including stocks, commodities, foreign exchange, and cryptocurrencies, by efficiently capturing domain-specific volatility patterns. This work not only sets a new standard for predictive accuracy in stock market modelling but also presents an invaluable tool for those navigating the complexities of modern financial markets.