Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting
Kishore Kumar Sahu, Sarat Chandra Nayak, Himansu Sekhar Behera · International Journal of Swarm Intelligence Research · 2022
Model with better learning ability and lower structural complexity is desirous for accurate exchange rate forecasting. Faster convergence to optimal solutions has always been a goal for the researcher in building forecasting models. And this is achieved by extreme learning machines (ELMs) due to their single hidden layer architecture and superior generalization ability. ELM is a simple training algorithm used to find the hidden-output layer weights by a random selection of input-hidden layer weights. Metaheuristics algorithms like Fireworks algorithm (FWA), Chemical reaction optimization (CRO), and Teaching learning-based optimization (TLBO) are employed to pre-train the ELM owing to their fewer optimizing parameters. This article aims to pre-train ELM using the said metaheuristics separately, ensuring the optimal solution of a single feedforward network (SLFN) with improved accuracy. The pre-trained ELMs provide accurate results. The same was verified using other primitive optimization algorithms