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An assessment of time series methods in metal price forecasting

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Cited by:

  1. 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.
  2. Arroyo Marioli,Francisco & Khadan,Jeetendra & Ohnsorge,Franziska Lieselotte & Yamazaki,Takefumi, 2023. "Forecasting Industrial Commodity Prices : Literature Review and a Model Suite," Policy Research Working Paper Series 10611, The World Bank.
  3. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
  4. Madziwa, Lawrence & Pillalamarry, Mallikarjun & Chatterjee, Snehamoy, 2022. "Gold price forecasting using multivariate stochastic model," Resources Policy, Elsevier, vol. 76(C).
  5. Monge, Manuel & Gil-Alana, Luis A., 2021. "Lithium industry and the U.S. crude oil prices. A fractional cointegration VAR and a Continuous Wavelet Transform analysis," Resources Policy, Elsevier, vol. 72(C).
  6. Ewald, Christian Oliver & Li, Yaoyu, 2024. "The role of news sentiment in salmon price prediction using deep learning," Journal of Commodity Markets, Elsevier, vol. 36(C).
  7. 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).
  8. Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
  9. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
  10. Sheng‐Tun Li & Kuei‐Chen Chiu & Chien‐Chang Wu, 2023. "Apply big data analytics for forecasting the prices of precious metals futures to construct a hedging strategy for industrial material procurement," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 942-959, March.
  11. Bielak, Łukasz & Grzesiek, Aleksandra & Janczura, Joanna & Wyłomańska, Agnieszka, 2021. "Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling," Resources Policy, Elsevier, vol. 74(C).
  12. Konstantinos Oikonomou & Dimitris Damigos, 2025. "Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 37-49, March.
  13. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
  14. 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.
  15. He, Zhichao & Huang, Jianhua, 2023. "A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correction," Resources Policy, Elsevier, vol. 86(PB).
  16. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
  17. Devendra Joshi & Premkumar Chithaluru & Divya Anand & Fahima Hajjej & Kapil Aggarwal & Vanessa Yelamos Torres & Ernesto Bautista Thompson, 2023. "RETRACTED: An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
  18. Gil-Alana, Luis A. & Tripathy, Trilochan, 2014. "Modelling volatility persistence and asymmetry: A Study on selected Indian non-ferrous metals markets," Resources Policy, Elsevier, vol. 41(C), pages 31-39.
  19. He, Kaijian & Lu, Xingjing & Zou, Yingchao & Keung Lai, Kin, 2015. "Forecasting metal prices with a curvelet based multiscale methodology," Resources Policy, Elsevier, vol. 45(C), pages 144-150.
  20. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).
  21. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
  22. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
  23. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
  24. Zhou, Jianguo & Xu, Zhongtian, 2023. "A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices," Resources Policy, Elsevier, vol. 80(C).
  25. Addison, Tony & Ghoshray, Atanu, 2023. "Discerning trends in international metal prices in the presence of nonstationary volatility," Resource and Energy Economics, Elsevier, vol. 71(C).
  26. Ge, Lei & Huang, Qiwei & Zhu, Fengshuang & Chen, Shun, 2025. "Advanced time series forecasting for commodities: Insights from the FEDformer model," Energy Economics, Elsevier, vol. 147(C).
  27. Mensi, Walid & Nekhili, Ramzi & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Quantile dependencies between precious and industrial metals futures and portfolio management," Resources Policy, Elsevier, vol. 73(C).
  28. Zhao, Jue & Hosseini, Shahab & Chen, Qinyang & Jahed Armaghani, Danial, 2023. "Super learner ensemble model: A novel approach for predicting monthly copper price in future," Resources Policy, Elsevier, vol. 85(PB).
  29. Yifei Zhao & Jianhong Chen & Hideki Shimada & Takashi Sasaoka, 2023. "Non-Ferrous Metal Price Point and Interval Prediction Based on Variational Mode Decomposition and Optimized LSTM Network," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
  30. Tapia Cortez, Carlos A. & Hitch, Michael & Sammut, Claude & Coulton, Jeff & Shishko, Robert & Saydam, Serkan, 2018. "Determining the embedding parameters governing long-term dynamics of copper prices," Chaos, Solitons & Fractals, Elsevier, vol. 111(C), pages 186-197.
  31. Viviana Fernández, 2006. "Forecasting crude oil and natural gas spot prices by classification methods," Documentos de Trabajo 229, Centro de Economía Aplicada, Universidad de Chile.
  32. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
  33. Ribeiro, Celma O. & Oliveira, Sydnei M., 2011. "A hybrid commodity price-forecasting model applied to the sugar–alcohol sector," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(2), pages 1-19.
  34. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
  35. Kshitij Kakade & Aswini Kumar Mishra & Kshitish Ghate & Shivang Gupta, 2022. "Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH‐LSTM based Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 103-117, April.
  36. Cifuentes, Sebastián & Cortazar, Gonzalo & Ortega, Hector & Schwartz, Eduardo S., 2020. "Expected prices, futures prices and time-varying risk premiums: The case of copper," Resources Policy, Elsevier, vol. 69(C).
  37. 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.
  38. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
  39. Apostolos Ampountolas, 2024. "Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models," IJFS, MDPI, vol. 12(3), pages 1-20, June.
  40. Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
  41. Adibi, Nabiollah & Ataee-pour, Majid, 2015. "Decreasing minerals׳ revenue risk by diversification of mineral production in mineral rich countries," Resources Policy, Elsevier, vol. 45(C), pages 121-129.
  42. Viviana Fernandez, 2008. "Traditional versus novel forecasting techniques: how much do we gain?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 637-648.
  43. Pincheira, Pablo & Hardy, Nicolas, 2018. "Forecasting Base Metal Prices with Commodity Currencies," MPRA Paper 83564, University Library of Munich, Germany.
  44. Wang, Jianzhou & Niu, Xinsong & Zhang, Linyue & Lv, Mengzheng, 2021. "Point and interval prediction for non-ferrous metals based on a hybrid prediction framework," Resources Policy, Elsevier, vol. 73(C).
  45. Fernandez, Viviana, 2007. "Wavelet- and SVM-based forecasts: An analysis of the U.S. metal and materials manufacturing industry," Resources Policy, Elsevier, vol. 32(1-2), pages 80-89.
  46. Hachmi Ben Ameur & Sahbi Boubaker & Zied Ftiti & Wael Louhichi & Kais Tissaoui, 2024. "Forecasting commodity prices: empirical evidence using deep learning tools," Annals of Operations Research, Springer, vol. 339(1), pages 349-367, August.
  47. 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).
  48. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2013. "Forecasting metal prices: Do forecasters herd?," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 150-158.
  49. Marek Kwas & Michał Rubaszek, 2021. "Forecasting Commodity Prices: Looking for a Benchmark," Forecasting, MDPI, vol. 3(2), pages 1-13, June.
  50. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2020. "Spillovers, integration and causality in LME non-ferrous metal markets," Journal of Commodity Markets, Elsevier, vol. 17(C).
  51. 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.
  52. Sroka Łukasz, 2022. "Applying Block Bootstrap Methods in Silver Prices Forecasting," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(2), pages 15-29, June.
  53. Muttaqin, Benazir Imam Arif & Ciptomulyono, Udisubakti & Siswanto, Nurhadi, 2025. "Optimizing cut-off grades under stochastic price: A model for open-pit lateritic nickel mining with multiple products," Resources Policy, Elsevier, vol. 106(C).
  54. 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.
  55. Esma Kahraman & Ozlem Akay, 2023. "Comparison of exponential smoothing methods in forecasting global prices of main metals," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(3), pages 427-435, September.
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