A Study of Biology-inspired Algorithms Applied to Long Short-Term Memory Network Training for Time Series Forecasting
Liliya Anatolievna Demidova, Artyom V. Gorchakov · 2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · 2021
Recent research introduced plenty of approaches to time series forecasting. Long short-term memory (LSTM) is a widely studied and effective recurrent artificial neural network architecture commonly used in time series prediction. LSTM networks are often trained using gradient-based methods. Such methods might be prone to premature convergence, and this affects prediction performance. In this paper, we consider a biology-inspired approach to LSTM loss function optimization. We compare the performance of different LSTM networks trained with backpropagation and using biology-inspired algorithms, including the Genetic Algorithm, Particle Swarm optimization, and Fish School Search. According to the obtained results, the LSTM network trained with the chaotic Fish School Search algorithm with exponential step decay produces the most accurate predictions in the considered time series forecasting problems.