Data analytics and optimised machine learning algorithm to analyse coffee commodity prices Online publication date: Mon, 07-Nov-2022
by Nguyen Duy Tan; Hwang Chan Yu; Le Ngoc Bao Long; Sam-Sang You
International Journal of Sustainable Agricultural Management and Informatics (IJSAMI), Vol. 8, No. 4, 2022
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.
Online publication date: Mon, 07-Nov-2022
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sustainable Agricultural Management and Informatics (IJSAMI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com