IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v59y2018icp95-102.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420718300904
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2018.06.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    3. Labys, W C & Lesourd, J B & Badillo, D, 1998. "The existence of metal price cycles," Resources Policy, Elsevier, vol. 24(3), pages 147-155, September.
    4. 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.
    5. Qiang Yang & Xindong Wu, 2006. "10 Challenging Problems In Data Mining Research," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 597-604.
    6. Aki-Hiro Sato, 2012. "A Comprehensive Analysis of Time Series Segmentation on the Japanese Stock Prices," Papers 1205.0332, arXiv.org, revised Mar 2013.
    7. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. Reboredo, Juan C. & Ugolini, Andrea, 2020. "Price spillovers between rare earth stocks and financial markets," Resources Policy, Elsevier, vol. 66(C).
    9. 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.
    10. 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.
    11. 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).
    12. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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.
    3. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
    4. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    5. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.
    6. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
    7. 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.
    8. Huijian Dong & Xiaomin Guo & Han Reichgelt & Ruizhi Hu, 2020. "Predictive power of ARIMA models in forecasting equity returns: a sliding window method," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 549-566, October.
    9. Seiler, Volker, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 160-179.
    10. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    11. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    12. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    13. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    14. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    15. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    16. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    17. Li, Ning & Li, Jiaojiao & Wang, Qizhou & Yan, Dairong & Wang, Liguan & Jia, Mingtao, 2024. "A novel copper price forecasting ensemble method using adversarial interpretive structural model and sparrow search algorithm," Resources Policy, Elsevier, vol. 91(C).
    18. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    19. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    20. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:59:y:2018:i:c:p:95-102. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.