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Financial time series forecasting model based on CEEMDAN and LSTM

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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. Seyed Mehrzad Asaad Sajadi & Pouya Khodaee & Ehsan Hajizadeh & Sabri Farhadi & Sohaib Dastgoshade & Bo Du, 2022. "Deep Learning-Based Methods for Forecasting Brent Crude Oil Return Considering COVID-19 Pandemic Effect," Energies, MDPI, vol. 15(21), pages 1-23, October.
  3. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
  4. Jiang, Minqi & Liu, Jiapeng & Zhang, Lu & Liu, Chunyu, 2020. "An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
  5. Zheng, Chengli & Su, Kuangxi & Yao, Yinhong, 2021. "Hedging futures performance with denoising and noise-assisted strategies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
  6. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
  7. Shi, Yu & Song, Xianzhi & Song, Guofeng, 2021. "Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network," Applied Energy, Elsevier, vol. 282(PA).
  8. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
  9. Matteo Miani & Matteo Dunnhofer & Christian Micheloni & Andrea Marini & Nicola Baldo, 2021. "Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit," Sustainability, MDPI, vol. 13(17), pages 1-18, August.
  10. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
  11. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
  12. Wen-Jie Liu & Yu-Ting Bai & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong, 2022. "Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-21, September.
  13. Wei Jiang & Jianzhong Zhou & Yanhe Xu & Jie Liu & Yahui Shan, 2019. "Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model," Complexity, Hindawi, vol. 2019, pages 1-18, October.
  14. Wei-Chang Yeh & Yu-Hsin Hsieh & Chia-Ling Huang, 2022. "Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm," Papers 2211.12839, arXiv.org.
  15. Axelsson, Birger & Song, Han-Suck, 2023. "Univariate Forecasting for REITs with Deep Learning: A Comparative Analysis with an ARIMA Model," Working Paper Series 23/10, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance, revised 14 Nov 2023.
  16. Li, Hongtao & Bai, Juncheng & Li, Yongwu, 2019. "A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
  17. Carmina Fjellstrom, 2022. "Long Short-Term Memory Neural Network for Financial Time Series," Papers 2201.08218, arXiv.org.
  18. Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong & Sun, Qiming, 2020. "A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system," Renewable Energy, Elsevier, vol. 161(C), pages 1244-1259.
  19. Fuping Liu & Ying Liu & Chen Yang & Ruixun Lai, 2022. "A New Precipitation Prediction Method Based on CEEMDAN-IWOA-BP Coupling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4785-4797, September.
  20. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
  21. Yun Chen & Chengwei Liang & Dengcheng Liu & Qingren Niu & Xinke Miao & Guangyu Dong & Liguang Li & Shanbin Liao & Xiaoci Ni & Xiaobo Huang, 2022. "Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction," Energies, MDPI, vol. 16(1), pages 1-20, December.
  22. Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
  23. Danijel Jevtic & Romain Deleze & Joerg Osterrieder, 2022. "AI for trading strategies," Papers 2208.07168, arXiv.org.
  24. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
  25. Qiutong Guo & Shun Lei & Qing Ye & Zhiyang Fang, 2021. "MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price," Papers 2105.00707, arXiv.org.
  26. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
  27. Jujie Wang & Yinan Liao & Zhenzhen Zhuang & Dongming Gao, 2021. "An Optimal Weighted Combined Model Coupled with Feature Reconstruction and Deep Learning for Multivariate Stock Index Forecasting," Mathematics, MDPI, vol. 9(21), pages 1-20, October.
  28. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  29. Guangji Tong & Zhiwei Yin, 2022. "Adaptive Trading System of Assets for International Cooperation in Agricultural Finance Based on Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1557-1576, April.
  30. Dongsu Kim & Yongjun Lee & Kyungil Chin & Pedro J. Mago & Heejin Cho & Jian Zhang, 2023. "Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
  31. Yoonjae Noh & Jong-Min Kim & Soongoo Hong & Sangjin Kim, 2023. "Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
  32. Peiwan Wang & Lu Zong & Ye Ma, 2019. "An Integrated Early Warning System for Stock Market Turbulence," Papers 1911.12596, arXiv.org.
  33. Qing Zhu & Renxian Zuo & Shan Liu & Fan Zhang, 2020. "Online dynamic group-buying community analysis based on high frequency time series simulation," Electronic Commerce Research, Springer, vol. 20(1), pages 81-118, March.
  34. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
  35. Liu, Jiayue & Ye, Jimin & E, Jianwei, 2023. "A multi-scale forecasting model for CPI based on independent component analysis and non-linear autoregressive neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
  36. Meng, Huixing & Geng, Mengyao & Xing, Jinduo & Zio, Enrico, 2022. "A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena," Energy, Elsevier, vol. 261(PB).
  37. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
  38. Cheng Zhao & Ping Hu & Xiaohui Liu & Xuefeng Lan & Haiming Zhang, 2023. "Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction," Mathematics, MDPI, vol. 11(5), pages 1-13, February.
  39. Daehyeon PARK & Doojin RYU, 2021. "Forecasting Stock Market Dynamics using Bidirectional Long Short-Term Memory," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 22-34, June.
  40. Chuting Sun & Qi Wu & Xing Yan, 2023. "Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning," Papers 2301.07318, arXiv.org, revised Jan 2024.
  41. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
  42. Sanghyuk Yoo & Sangyong Jeon & Seunghwan Jeong & Heesoo Lee & Hosun Ryou & Taehyun Park & Yeonji Choi & Kyongjoo Oh, 2021. "Prediction of the Change Points in Stock Markets Using DAE-LSTM," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
  43. Seungho Baek & Kwan Yong Lee & Merih Uctum & Seok Hee Oh, 2020. "Robo-Advisors: Machine Learning in Trend-Following ETF Investments," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
  44. Yang, Shaobo & Deng, Zegui & Li, Xingfei & Zheng, Chongwei & Xi, Lintong & Zhuang, Jucheng & Zhang, Zhenquan & Zhang, Zhiyou, 2021. "A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast," Renewable Energy, Elsevier, vol. 173(C), pages 531-543.
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