文献类型：期刊文献

英文题名：Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods

作者：Li, Xiaobin[1];Sengupta, Tuhin[2];Mohammed, Kamel Si[3];Jamaani, Fouad[4]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200001, Peoples R China;[2]Indian Inst Management Ranchi, Dept Operat Management, 5th Floor, Suchana Bhawan, Meurs Rd, Audrey House, Jharkhand 834008, India;[3]Univ Ain Temouchent, Fac Econ & Management, Dept Econ, Ain Temouchent, Algeria;[4]Taif Univ, Coll Business Adm, Econ & Finance, POB 11099, Taif 21944, Saudi Arabia

年份：2023

卷号：82

外文期刊名：RESOURCES POLICY

收录：;EI(收录号：20231613896411);WOS:【SSCI(收录号:WOS:000984809300001)】；

语种：英文

外文关键词：Lithium price; China; Mineral resources; Machine learning; Forecasting; ANN

摘要：Combining lithium real-time series data with recently developed advanced Artificial Neural Networks (ANN) and Facebook Prophet (Fb-P) algorithms is of particular relevance for identifying and delivering policy-insightful patterns by learning from experimental data without being pre-conditioned and managing investment risk. The prime objective of this study is to forecast lithium mineral resource prices in China. This study uses the Fb-P and ANN techniques to estimate lithium prices utilizing daily historical data between 5 November 2018 and 1 November 2022. In doing so, the empirical estimates help to predict future prices until 20 April 2023. The findings of the Facebook Prophet technique demonstrate that lithium mineral pricing has a very high degree of accuracy and has a long short-term memory at differential frequency days intervals. In contrast to the current price of 572,500 yuan/tonne, it may have been noticed that the market would suddenly surge in the next six months, reaching more than 800000 yuan/tonne. The study attempts to draw novel implications in the context of mineral resource prices in China.