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

Xiaobin Li, Tuhin Sengupta, Kamel Si Mohammed and Fouad Jamaani

Resources Policy, 2023, vol. 82, issue C

Abstract: 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.

Keywords: Lithium price; China; Mineral resources; Machine learning; Forecasting; ANN (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:82:y:2023:i:c:s030142072300291x

DOI: 10.1016/j.resourpol.2023.103580

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