IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v102y2016icp365-374.html
   My bibliography  Save this item

Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

Citations

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


Cited by:

  1. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
  2. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  3. Di Zhu & Yinghong Wang & Fenglin Zhang, 2022. "Energy Price Prediction Integrated with Singular Spectrum Analysis and Long Short-Term Memory Network against the Background of Carbon Neutrality," Energies, MDPI, vol. 15(21), pages 1-20, October.
  4. Wang, Bin & Wang, Jun, 2021. "Energy futures price prediction and evaluation model with deep bidirectional gated recurrent unit neural network and RIF-based algorithm," Energy, Elsevier, vol. 216(C).
  5. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
  6. Ana Njegovanović, 2023. "The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty," International Journal of Social Science Studies, Redfame publishing, vol. 11(1), pages 54-71, January.
  7. Xing, Yani & Wang, Jun, 2019. "Statistical volatility duration and complexity of financial dynamics on Sierpinski gasket lattice percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 234-247.
  8. Chao Deng & Liang Ma & Taishan Zeng, 2021. "Crude Oil Price Forecast Based on Deep Transfer Learning: Shanghai Crude Oil as an Example," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
  9. Bugała, A. & Zaborowicz, M. & Boniecki, P. & Janczak, D. & Koszela, K. & Czekała, W. & Lewicki, A., 2018. "Short-term forecast of generation of electric energy in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 306-312.
  10. Huang, Lili & Wang, Jun, 2018. "Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network," Energy, Elsevier, vol. 151(C), pages 875-888.
  11. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
  12. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
  13. Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
  14. Guo, Jingjun & Zhao, Zhengling & Sun, Jingyun & Sun, Shaolong, 2022. "Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework," Resources Policy, Elsevier, vol. 77(C).
  15. Ramos & Pablo Negri & Martín Breitkopf & María Laura Ojeda, 2021. "From International to Regional Commodity Price Pass-through Using Self-Driven Recurrent Networks," Asociación Argentina de Economía Política: Working Papers 4513, Asociación Argentina de Economía Política.
  16. Zied Ftiti & Kais Tissaoui & Sahbi Boubaker, 2022. "On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach," Annals of Operations Research, Springer, vol. 313(2), pages 915-943, June.
  17. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
  18. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
  19. Linlin Zhao & Zhansheng Liu & Jasper Mbachu, 2019. "Energy Management through Cost Forecasting for Residential Buildings in New Zealand," Energies, MDPI, vol. 12(15), pages 1-24, July.
  20. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
  21. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
  22. Yani Xing & Jun Wang, 2020. "Linkages between global crude oil market volatility and financial market by complexity synchronization," Empirical Economics, Springer, vol. 59(5), pages 2405-2421, November.
  23. Jiang, Meihui & An, Haizhong & Jia, Xiaoliang & Sun, Xiaoqi, 2017. "The influence of global benchmark oil prices on the regional oil spot market in multi-period evolution," Energy, Elsevier, vol. 118(C), pages 742-752.
  24. Zhang, Yali & Wang, Jun, 2019. "Linkage influence of energy market on financial market by multiscale complexity synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 254-266.
  25. Yue Liu & Hao Dong & Pierre Failler, 2019. "The Oil Market Reactions to OPEC’s Announcements," Energies, MDPI, vol. 12(17), pages 1-15, August.
  26. Li, Ranran & Hu, Yucai & Heng, Jiani & Chen, Xueli, 2021. "A novel multiscale forecasting model for crude oil price time series," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
  27. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
  28. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
  29. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.
  30. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
  31. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
  32. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
  33. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
  34. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
  35. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
  36. Wenquan Jin & Israr Ullah & Shabir Ahmad & Dohyeun Kim, 2019. "Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
  37. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
  38. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
  39. Bouteska, Ahmed & Hajek, Petr & Fisher, Ben & Abedin, Mohammad Zoynul, 2023. "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network," Research in International Business and Finance, Elsevier, vol. 64(C).
  40. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
  41. Jiang, Ping & Wang, Biao & Li, Hongmin & Lu, Haiyan, 2019. "Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting," Energy, Elsevier, vol. 173(C), pages 468-482.
  42. Zhao, Shangmei & Chen, Xinyi & Zhang, Junhuan, 2019. "The systemic risk of China’s stock market during the crashes in 2008 and 2015," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 161-177.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.