Enhanced long short-term memory architectures for chaotic systems modeling: An extensive study on the Lorenz system.

Abstract

Despite recent advancements in machine learning algorithms, well-established models like the Long Short-Term Memory (LSTM) are still widely used for modeling tasks. This paper introduces an enhanced LSTM variant and explores its capabilities in multiple input single output chaotic system modeling, offering a large-scale analysis that focuses on LSTM gate-level architecture, the effects of noise, non-stationary and dynamic behavior modeling, system parameter drifts, and short- and long-term forecasting. The experimental evaluation is performed on datasets generated using MATLAB, where the Lorenz and Rössler system equations are implemented and simulated in various scenarios. The extended analysis reveals that a simplified, less complex LSTM-based architecture can be successfully employed for accurate chaotic system modeling without the need for complex deep learning methodologies. This new proposed model includes only three of the four standard LSTM gates, with other feedback modifications.