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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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    References listed on IDEAS

    as
    1. Chopra, Ritika & Magazzino, Cosimo & Shah, Muhammad Ibrahim & Sharma, Gagan Deep & Rao, Amar & Shahzad, Umer, 2022. "The role of renewable energy and natural resources for sustainable agriculture in ASEAN countries: Do carbon emissions and deforestation affect agriculture productivity?," Resources Policy, Elsevier, vol. 76(C).
    2. Kucevic, Daniel & Englberger, Stefan & Sharma, Anurag & Trivedi, Anupam & Tepe, Benedikt & Schachler, Birgit & Hesse, Holger & Srinivasan, Dipti & Jossen, Andreas, 2021. "Reducing grid peak load through the coordinated control of battery energy storage systems located at electric vehicle charging parks," Applied Energy, Elsevier, vol. 295(C).
    3. Xaviery N. Penisa & Michael T. Castro & Jethro Daniel A. Pascasio & Eugene A. Esparcia & Oliver Schmidt & Joey D. Ocon, 2020. "Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model," Energies, MDPI, vol. 13(20), pages 1-18, October.
    4. Aslam, Faheem & Zil-e-huma, & Bibi, Rashida & Ferreira, Paulo, 2022. "Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis," Resources Policy, Elsevier, vol. 75(C).
    5. Shi, Yangyan & Feng, Yu & Zhang, Qi & Shuai, Jing & Niu, Jiangxin, 2023. "Does China's new energy vehicles supply chain stock market have risk spillovers? Evidence from raw material price effect on lithium batteries," Energy, Elsevier, vol. 262(PA).
    6. Chao-Yu Guo & Yu-Chin Chou, 2020. "A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM)," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.
    7. Chen, Fu & Tiwari, Sunil & Mohammed, Kamel Si & Huo, Weidong & Jamróz, Paweł, 2023. "Minerals resource rent responses to economic performance, greener energy, and environmental policy in China: Combination of ML and ANN outputs," Resources Policy, Elsevier, vol. 81(C).
    8. Sadik-Zada, Elkhan Richard & Gatto, Andrea & Scharfenstein, Manuel, 2023. "Sustainable management of lithium and green hydrogen and long-run perspectives of electromobility," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    9. Marco Mele & Cosimo Magazzino & Nicolas Schneider & Antonia Rosa Gurrieri & Hêriş Golpira, 2022. "Innovation, income, and waste disposal operations in Korea: evidence from a spectral granger causality analysis and artificial neural networks experiments," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(2), pages 427-459, July.
    10. Hossain, Mohammad Razib & Singh, Sanjeet & Sharma, Gagan Deep & Apostu, Simona-Andreea & Bansal, Pooja, 2023. "Overcoming the shock of energy depletion for energy policy? Tracing the missing link between energy depletion, renewable energy development and decarbonization in the USA," Energy Policy, Elsevier, vol. 174(C).
    11. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
    12. Zheng, Xiaolei & Nguyen, Hoang & Bui, Xuan-Nam, 2021. "Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model," Resources Policy, Elsevier, vol. 74(C).
    13. Huang, Yu-ting & Bai, Yu-long & Yu, Qing-he & Ding, Lin & Ma, Yong-jie, 2022. "Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction," Resources Policy, Elsevier, vol. 79(C).
    14. Zhou, Na & Su, Hui & Wu, Qiaosheng & Hu, Shougeng & Xu, Deyi & Yang, Danhui & Cheng, Jinhua, 2022. "China's lithium supply chain: Security dynamics and policy countermeasures," Resources Policy, Elsevier, vol. 78(C).
    15. Jung Youn Mo & Wooyoung Jeon, 2018. "The Impact of Electric Vehicle Demand and Battery Recycling on Price Dynamics of Lithium-Ion Battery Cathode Materials: A Vector Error Correction Model (VECM) Analysis," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    16. Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
    17. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    18. Farid, Saqib & Kayani, Ghulam Mujtaba & Naeem, Muhammad Abubakr & Shahzad, Syed Jawad Hussain, 2021. "Intraday volatility transmission among precious metals, energy and stocks during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 72(C).
    19. Si Mohammed, K. & Mellit, A., 2023. "The relationship between oil prices and the indices of renewable energy and technology companies based on QQR and GCQ techniques," Renewable Energy, Elsevier, vol. 209(C), pages 97-105.
    20. Liu, Donghui & Gao, Xiangyun & An, Haizhong & Qi, Yabin & Wang, Ze & Jia, Nanfei & Chen, Zhihua, 2020. "Exploring behavior changes of the lithium market in China: Toward technology-oriented future scenarios," Resources Policy, Elsevier, vol. 69(C).
    21. Sharma, Gagan Deep & Verma, Mahesh & Shahbaz, Muhammad & Gupta, Mansi & Chopra, Ritika, 2022. "Transitioning green finance from theory to practice for renewable energy development," Renewable Energy, Elsevier, vol. 195(C), pages 554-565.
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