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Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models

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  • Riesgo García, María Victoria
  • Krzemień, Alicja
  • Manzanedo del Campo, Miguel Ángel
  • Escanciano García-Miranda, Carmen
  • Sánchez Lasheras, Fernando

Abstract

A time series can be thought of as a numerical organism with a continuous nature from a chronological point of view and something that is permanently updated. Up to this moment time series research related with their features, traits, and characteristics, is mainly focused on data mining, in order to discover hidden information or specific knowledge within the time series or their transformations. However, time series representation is crucial, as they are difficult to handle in their original structure due to their high dimensionality.

Suggested Citation

  • Riesgo García, María Victoria & Krzemień, Alicja & Manzanedo del Campo, Miguel Ángel & Escanciano García-Miranda, Carmen & Sánchez Lasheras, Fernando, 2018. "Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models," Resources Policy, Elsevier, vol. 59(C), pages 95-102.
    • Handle: RePEc:eee:jrpoli:v:59:y:2018:i:c:p:95-102
    DOI: 10.1016/j.resourpol.2018.06.003
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
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    5. Riesgo García, María Victoria & Krzemień, Alicja & Manzanedo del Campo, Miguel Ángel & Menéndez Álvarez, Mario & Gent, Malcolm Richard, 2017. "Rare earth elements mining investment: It is not all about China," Resources Policy, Elsevier, vol. 53(C), pages 66-76.
    6. Yin, Yi & Shang, Pengjian & Xia, Jianan, 2015. "Compositional segmentation of time series in the financial markets," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 399-412.
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    8. Aki-Hiro Sato, 2012. "A Comprehensive Analysis of Time Series Segmentation on the Japanese Stock Prices," Papers 1205.0332, arXiv.org, revised Mar 2013.
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    Cited by:

    1. Jiahao Chen & Jiahui Yi & Kailei Liu & Jinhua Cheng & Yin Feng & Chuandi Fang, 2023. "Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-19, October.
    2. Shuai Wang & Bo Cao & Runcai Bai & Guangwei Liu, 2025. "Determination of production capacity for open-pit coal mines under uncertainty: A model based on economies of scale," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-28, January.
    3. Guo, Qing & Mai, Zishan, 2024. "How do seasonal, significant events, and policies affect China's REE export prices? Based on deep learning perspective," Resources Policy, Elsevier, vol. 96(C).
    4. Zhang, Hongwei & Wei, Shiyao & Guo, Yaoqi & Gao, Wang, 2024. "Analyzing the interconnection between rare earth market and green economy: Time-varying effects of trade policy uncertainty," Resources Policy, Elsevier, vol. 97(C).
    5. Riesgo García, María Victoria & Krzemień, Alicja & Sáiz Bárcena, Lourdes Cecilia & Diego Álvarez, Isidro & Castañón Fernández, César, 2019. "Scoping studies of rare earth mining investments: Deciding on further project developments," Resources Policy, Elsevier, vol. 64(C).
    6. Yufeng Chen & Biao Zheng, 2019. "What Happens after the Rare Earth Crisis: A Systematic Literature Review," Sustainability, MDPI, vol. 11(5), pages 1-26, March.
    7. Caner Özdurak & Veysel Ulusoy, 2020. "Spillovers from the Slowdown in China on Financial and Energy Markets: An Application of VAR–VECH–TARCH Models," IJFS, MDPI, vol. 8(3), pages 1-17, August.
    8. Marta Matyjaszek & Gregorio Fidalgo Valverde & Alicja Krzemień & Krzysztof Wodarski & Pedro Riesgo Fernández, 2020. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production," Energies, MDPI, vol. 13(8), pages 1-15, April.
    9. Ivan Borisov Todorov & Fernando Sánchez Lasheras, 2022. "Forecasting Applied to the Electricity, Energy, Gas and Oil Industries: A Systematic Review," Mathematics, MDPI, vol. 10(21), pages 1-15, October.
    10. Zheng, Biao & Zhang, Yuquan & Chen, Yufeng, 2021. "Asymmetric connectedness and dynamic spillovers between renewable energy and rare earth markets in China: Evidence from firms’ high-frequency data," Resources Policy, Elsevier, vol. 71(C).
    11. Sterba, Jiri & Krzemień, Alicja & Riesgo Fernández, Pedro & Escanciano García-Miranda, Carmen & Fidalgo Valverde, Gregorio, 2019. "Lithium mining: Accelerating the transition to sustainable energy," Resources Policy, Elsevier, vol. 62(C), pages 416-426.
    12. Reboredo, Juan C. & Ugolini, Andrea, 2020. "Price spillovers between rare earth stocks and financial markets," Resources Policy, Elsevier, vol. 66(C).
    13. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    14. Hualing Lin & Qiubi Sun & Sheng-Qun Chen, 2020. "Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM," Sustainability, MDPI, vol. 12(6), pages 1-19, March.
    15. Madaleno, Mara & Taskin, Dilvin & Dogan, Eyup & Tzeremes, Panayiotis, 2023. "A dynamic connectedness analysis between rare earth prices and renewable energy," Resources Policy, Elsevier, vol. 85(PB).
    16. Buelga Díaz, Arturo & Diego Álvarez, Isidro & Castañón Fernández, César & Krzemień, Alicja & Iglesias Rodríguez, Francisco Javier, 2021. "Calculating ultimate pit limits and determining pushbacks in open-pit mining projects," Resources Policy, Elsevier, vol. 72(C).

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