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

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

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.

Suggested Citation

  • Li, Xiaobin & Sengupta, Tuhin & Si Mohammed, Kamel & Jamaani, Fouad, 2023. "Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods," Resources Policy, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jrpoli:v:82:y:2023:i:c:s030142072300291x
    DOI: 10.1016/j.resourpol.2023.103580
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    3. Ghosh, Indranil & Jana, Rabin K., 2024. "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
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    6. Sarwar, Suleman & Aziz, Ghazala & Waheed, Rida & Morales, Lucía, 2024. "Forecasting the mineral resource rent through the inclusion of economy, environment and energy: Advanced machine learning and deep learning techniques," Resources Policy, Elsevier, vol. 90(C).

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