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Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory

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  1. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
  2. Anggi Putri Kurniadi & Hasdi Aimon & Zamroni Salim & Ragimun Ragimun & Adang Sonjaya & Sigit Setiawan & Viktor Siagian & Lokot Zein Nasution & R Nurhidajat & Mutaqin Mutaqin & Joko Sabtohadi, 2024. "Analysis of Existing and Forecasting for Coal and Solar Energy Consumption on Climate Change in Asia Pacific: New Evidence for Sustainable Development Goals," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 352-359, July.
  3. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
  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. 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.
  6. Parviz Sohrabi & Behshad Jodeiri Shokri & Hesam Dehghani, 2023. "Predicting coal price using time series methods and combination of radial basis function (RBF) neural network with time series," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 207-216, June.
  7. Gu, Fu & Wang, Jiqiang & Guo, Jianfeng & Fan, Ying, 2020. "How the supply and demand of steam coal affect the investment in clean energy industry? Evidence from China," Resources Policy, Elsevier, vol. 69(C).
  8. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.
  9. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Lu, Haiyan & Zhang, Linyue, 2022. "A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory," Resources Policy, Elsevier, vol. 79(C).
  10. Yu-Min Lian & Chia-Hsuan Li & Yi-Hsuan Wei, 2021. "Machine Learning and Time Series Models for VNQ Market Predictions," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 11(5), pages 1-2.
  11. Syed Naeem Haider & Qianchuan Zhao & Xueliang Li, 2020. "Cluster-Based Prediction for Batteries in Data Centers," Energies, MDPI, vol. 13(5), pages 1-17, March.
  12. Xu, Mengjie & Li, Xiang & Li, Qianwen & Sun, Chuanwang, 2024. "LNBi-GRU model for coal price prediction and pattern recognition analysis," Applied Energy, Elsevier, vol. 365(C).
  13. Hajirahimi, Zahra & Khashei, Mehdi, 2022. "Series Hybridization of Parallel (SHOP) models for time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
  14. Becerra, Miguel & Jerez, Alejandro & Garcés, Hugo O. & Demarco, Rodrigo, 2022. "Copper price: A brief analysis of China’s impact over its short-term forecasting," Resources Policy, Elsevier, vol. 75(C).
  15. Mutele, Litshedzani & Carranza, Emmanuel John M., 2024. "Statistical analysis of gold production in South Africa using ARIMA, VAR and ARNN modelling techniques: Extrapolating future gold production, Resources–Reserves depletion, and Implication on South Afr," Resources Policy, Elsevier, vol. 93(C).
  16. Jonek-Kowalska, Izabela, 2022. "Towards the reduction of CO2 emissions. Paths of pro-ecological transformation of energy mixes in European countries with an above-average share of coal in energy consumption," Resources Policy, Elsevier, vol. 77(C).
  17. 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).
  18. Wu, Siping & Xia, Guilin & Liu, Lang, 2023. "A novel decomposition integration model for power coal price forecasting," Resources Policy, Elsevier, vol. 80(C).
  19. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
  20. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
  21. Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).
  22. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
  23. Khoshalan, Hasel Amini & Shakeri, Jamshid & Najmoddini, Iraj & Asadizadeh, Mostafa, 2021. "Forecasting copper price by application of robust artificial intelligence techniques," Resources Policy, Elsevier, vol. 73(C).
  24. 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.
  25. 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).
  26. Guangyong Zhang & Lixin Tian & Min Fu & Bingyue Wan & Wenbin Zhang, 2020. "Research on the Transmission Ability of China’s Thermal Coal Price Information Based on Directed Limited Penetrable Interdependent Network," Sustainability, MDPI, vol. 12(18), pages 1-23, September.
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