Tuned long short-term memory model for Ethereum price forecasting via an arithmetic optimization algorithm

Abstract

Machine learning as a subset of artificial intelligence presents a promising set of algorithms for tackling increasingly complex challenges. A notable ability of this subgroup of algorithms to tackle tasks without explicit programming coupled with the expanding availability of computational resources and information transparency has made it possible to utilize algorithms to forecast prices. In recent years, cryptocurrency has increased in popularity and has seen wider adoption as a payment method. Cryptocurrency trading and mining have become a potentially very lucrative venture. However, due to the instability of cryptocurrency prices, casting accurate predictions can be quite challenging. A novel way of approaching this challenge is by tackling it through time-series forecasting. A particularly promising method for tackling this type of problem is through the utilization of long-short-term memory artificial neural networks to attain accurate prediction results. However, the forecasting accuracy of machine learning models is highly dependent on adequate hyperparameter settings. Thus, this work presents an improved variation of the arithmetic optimization algorithm, tasked with selecting the best values of a long-short term neural network casting price predictions. The presented approach has been evaluated on publicly available real-world Ethereum trading price data. The attained results of a comparative analysis against several popular metaheuristics indicate that the presented method achieved excellent results, and outperformed aforementioned algorithms in one and four-step ahead predictions.