Chaos theory meets deep learning: A new approach to time series forecasting

We explore the influence and advantages of integrating chaotic systems with deep learning for time series forecasting in this paper. It proposes a novel deep learning method based on the Chen system, which leverages the randomness, sensitivity, and diversity of chaotic mapping to enhance the performance and efficiency of deep learning models. We introduce a deep learning framework that integrates chaotic systems, providing an innovative and effective approach for time series forecasting. The research utilizes three different types of deep learning models as baselines—Long Short-Term Memory, Neural Basis Expansion Analysis, and Transformer—and compares them with their chaotic counterparts to demonstrate the impact of chaotic systems on various deep learning architectures. Experimental validation is conducted on thirteen available time series datasets, assessing the models' forecasting accuracy, runtime, and resource occupancy. The effectiveness and superiority of the chaotic deep learning method are verified across diverse datasets, including stock markets, electricity, and air quality, showcasing significant improvements over traditional model initialization methods.