Title: Data analytics and optimised machine learning algorithm to analyse coffee commodity prices
Authors: Nguyen Duy Tan; Hwang Chan Yu; Le Ngoc Bao Long; Sam-Sang You
Addresses: Department of Logistics, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, South Korea ' Department of Psychology, Georgetown University, 3700 O St NW, Washington DC 20057, USA ' Department of Logistics, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, South Korea ' Mechanical Engineering, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, South Korea
Abstract: Coffee has long become a familiar and famous drink all over the world. Due to supply chain disruptions, the coffee market is vibrant and changing rapidly. In this article, the time series data on world coffee price will be analysed by using data analytics such as Lyapunov exponent (LE), entropy, and Hurst exponent (HE). By denoising time series data using wavelet decomposition, the echo state network (ESN) model is presented for forecasting time series data. When the coffee commodity price is fluctuating and affecting to production planning and scheduling of supply chain management under market uncertainty, it is necessary to improve the accuracy and efficiency of the prediction method by employing the grey wolf optimisation (GWO) algorithm. By employing the hybrid technique, the predictive analytics models could efficiently estimate the actual prices. The GWO algorithm enhances a machining learning model's performance for ensuring optimal forecasting with increased accuracy.
Keywords: coffee price; time series data; wavelet decomposition; echo state network; ESN; grey wolf optimiser.
International Journal of Sustainable Agricultural Management and Informatics, 2022 Vol.8 No.4, pp.345 - 366
Received: 30 Apr 2022
Accepted: 08 Jul 2022
Published online: 07 Nov 2022 *