Data analytics and optimised machine learning algorithm to analyse coffee commodity prices. (26th October 2022)

• Record Type: Journal Article
• Authors: Tan, Nguyen Duy; Yu, Hwang Chan; Long, Le Ngoc Bao; You, Sam-Sang
• Is Part Of: International journal of sustainable agricultural management and informatics. Volume 8:Number 4(2022)
• Page Start: 345
• Page End: 366
• Publication Date: 2022-10-26
• Subjects: coffee price -- time series data -- wavelet decomposition -- echo state network -- ESN -- grey wolf optimiser; Sustainable agriculture -- Management -- Periodicals; Sustainable agriculture -- Data processing -- Periodicals; Agricultural productivity -- Management -- Periodicals; Agricultural productivity -- Data processing -- Periodicals; Agricultural resources -- Data processing -- Periodicals; Agricultural systems -- Management -- Periodicals; Agricultural systems -- Data processing -- Periodicals; 338.1
• Languages: English
• ISSNs: 2054-5819

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.