2DS-L: A dynamical system decomposition of signal approach to learning with application in time series prediction

Abstract

In this research, we propose a novel approach for time series forecasting using dynamical systems, signal processing, and Neural Networks, which we named 2DS-L. As dynamical systems frequently display complex and nonlinear behavior, accurately modeling time series data's evolving dynamics and interdependencies is crucial to time series prediction. Managing high -dimensional and complex datasets is another challenge in machine learning time series prediction. Using mathematical relationships, the proposed method decomposes the time series signal and establishes a connection between its dynamical system and a neural network. The performance of the 2DS-L method was compared with other popular methods like LSTM, GRU, and DEANN using stock price data, climate change data, and biology data. The results showed that despite having only 35% of the training parameters of LSTM and 50% GRU, the 2DS-L method's performance was better or close to it. This paper's approach offers an efficient and accurate forecasting technique that could be valuable in various domains, including finance, climate, and biology.