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Financial time series forecasting model based on CEEMDAN and LSTM

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Cited by:

  1. Muhammad Ali Faisal & Murat Donduran, 2025. "A Two-Stage Analysis of Interaction Between Stock and Exchange Rate Markets: Evidence from Turkey," Annals of Data Science, Springer, vol. 12(1), pages 171-198, February.
  2. Zefan Dong & Yonghui Zhou, 2024. "A Novel Hybrid Model for Financial Forecasting Based on CEEMDAN-SE and ARIMA-CNN-LSTM," Mathematics, MDPI, vol. 12(16), pages 1-16, August.
  3. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
  4. Yun Zhou & Xuxu Zhu, 2025. "Forecasting USD/RMB exchange rate using the ICEEMDAN‐CNN‐LSTM model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 200-215, January.
  5. Viviane Naimy & Tatiana Abou Chedid & Omar Abou Saleh & Nicolas Bitar, 2025. "Redefining volatility forecasting in the aerospace and defense sector: application of CEEMDAN-GARCH models," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-16, December.
  6. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
  7. 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.
  8. 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).
  9. 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.
  10. Yi Xiao & Minghu Xie & Yi Hu & Ming Yi, 2023. "Effective multi‐step ahead container throughput forecasting under the complex context," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1823-1843, November.
  11. Paul Bilokon & Yitao Qiu, 2023. "Transformers versus LSTMs for electronic trading," Papers 2309.11400, arXiv.org.
  12. 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.
  13. 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.
  14. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
  15. 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.
  16. Varshini, Anu & Kayal, Parthajit & Maiti, Moinak, 2024. "How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample," Resources Policy, Elsevier, vol. 92(C).
  17. Ahmad GHAREEB & Mihai Daniel ROMAN, 2025. "Forecasting Stock Prices For Maritime Shipping Company In Covid-19 Period Using Multivariate Multi-Step Multi-Step Convolutional Neural Network - Bidirectional Long Short-Term Memory," Eastern European Journal for Regional Studies (EEJRS), Center for Studies in European Integration (CSEI), Academy of Economic Studies of Moldova (ASEM), vol. 11(1), pages 6-24, June.
  18. 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.
  19. Nacira Agram & Bernt Øksendal & Jan Rems, 2024. "Deep learning for quadratic hedging in incomplete jump market," Digital Finance, Springer, vol. 6(3), pages 463-499, September.
  20. 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).
  21. 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).
  22. Peiwan Wang & Lu Zong & Ye Ma, 2019. "An Integrated Early Warning System for Stock Market Turbulence," Papers 1911.12596, arXiv.org.
  23. García-Figal, Alejandro & García-Borroto, Milton & Lage-Codorniu, Carlos & Mulet, Roberto & Lage-Castellanos, Alejandro, 2025. "Dynamics and predictability in informal currency markets: The case of the Cuban Peso," Emerging Markets Review, Elsevier, vol. 69(C).
  24. 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).
  25. Nacira Agram & Bernt {O}ksendal & Jan Rems, 2024. "Deep learning for quadratic hedging in incomplete jump market," Papers 2407.13688, arXiv.org.
  26. Hiridik Rajendran & Parthajit Kayal & MOINAK Maiti, 2025. "A Multipurpose hybrid forecasting framework for economic stress scenarios: evidence from agriculture and energy sectors," Future Business Journal, Springer, vol. 11(1), pages 1-17, December.
  27. Zhicheng Xiao & Lijuan Yu & Huajun Zhang & Xuetao Zhang & Yixin Su, 2023. "HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model," Mathematics, MDPI, vol. 11(22), pages 1-24, November.
  28. Olcay Ozupek & Reyat Yilmaz & Bita Ghasemkhani & Derya Birant & Recep Alp Kut, 2024. "A Novel Hybrid Model (EMD-TI-LSTM) for Enhanced Financial Forecasting with Machine Learning," Mathematics, MDPI, vol. 12(17), pages 1-36, September.
  29. 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).
  30. Pedro Reis & Ana Paula Serra & Jo~ao Gama, 2025. "The Role of Deep Learning in Financial Asset Management: A Systematic Review," Papers 2503.01591, arXiv.org.
  31. 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.
  32. 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.
  33. Yaoxun Deng & Guobin Fang & Jun Zhang & Huimin Ma, 2024. "RETRACTED ARTICLE: Dynamic Connectedness Among Oil, Food Commodity, and Renewable Energy Markets: Novel Perspective from Quantile Dependence and Deep Learning," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 9935-9974, September.
  34. Pan, Yidi & Hu, Wenqi & Ge, Xinlei & Lin, Aijing, 2025. "A novel forecasting method for time series based on vector visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 677(C).
  35. Ruiqing Liu & Yonghong Wang, 2025. "RETRACTED ARTICLE: Enhancing Enterprise Value Creation Through Intelligent Digital Transformation of the Value Chain: A Deep Learning and Edge Computing Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 2601-2619, March.
  36. Hau, Liya & Xue, Wenxin & Liu, Qigui, 2025. "Multiscale information network among fossil energy, renewable energy and ESG investment under the Russo-Ukrainian conflict," Research in International Business and Finance, Elsevier, vol. 80(C).
  37. Gyana Ranjan Patra & Mihir Narayan Mohanty, 2023. "Price Prediction of Cryptocurrency Using a Multi-Layer Gated Recurrent Unit Network with Multi Features," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1525-1544, December.
  38. Zeyu Zhang & Xiaoqian Liu & Xiling Zhang & Zhishan Yang & Jian Yao, 2024. "Carbon Price Forecasting Using Optimized Sliding Window Empirical Wavelet Transform and Gated Recurrent Unit Network to Mitigate Data Leakage," Energies, MDPI, vol. 17(17), pages 1-22, August.
  39. László Vancsura & Tibor Tatay & Tibor Bareith, 2025. "Navigating AI-Driven Financial Forecasting: A Systematic Review of Current Status and Critical Research Gaps," Forecasting, MDPI, vol. 7(3), pages 1-49, July.
  40. 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).
  41. Longyue Liang & Bo Liu & Zhi Su & Xuanye Cai, 2024. "Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2540-2571, November.
  42. Mengchen Zhao & Santiago Gomez-Rosero & Hooman Nouraei & Craig Zych & Miriam A. M. Capretz & Ayan Sadhu, 2024. "Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning," Energies, MDPI, vol. 17(7), pages 1-24, April.
  43. Zhiyuan Pei & Jianqi Yan & Jin Yan & Bailing Yang & Xin Liu, 2025. "Multi-Scale TsMixer: A Novel Time-Series Architecture for Predicting A-Share Stock Index Futures," Mathematics, MDPI, vol. 13(9), pages 1-19, April.
  44. 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).
  45. 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.
  46. 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.
  47. 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).
  48. 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.
  49. 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.
  50. 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.
  51. Jin, Zhao & Li, Xuebin & Qiu, Zhiqiang & Li, Fei & Kong, Erdan & Li, Bo, 2025. "A data-driven framework for lithium-ion battery RUL using LSTM and XGBoost with feature selection via Binary Firefly Algorithm," Energy, Elsevier, vol. 314(C).
  52. 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.
  53. Yang, Qu & Yu, Yuanyuan & Dai, Dongsheng & He, Qian & Lin, Yu, 2024. "Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
  54. Maosheng Li & Chen Zhang, 2024. "An Urban Metro Section Flow Forecasting Method Combining Time Series Decomposition and a Generative Adversarial Network," Sustainability, MDPI, vol. 16(2), pages 1-19, January.
  55. 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).
  56. 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).
  57. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
  58. 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.
  59. 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.
  60. Carmina Fjellstrom, 2022. "Long Short-Term Memory Neural Network for Financial Time Series," Papers 2201.08218, arXiv.org.
  61. Yang, Kailing & Zhang, Xi & Luo, Haojia & Hou, Xianping & Lin, Yu & Wu, Jingyu & Yu, Liang, 2024. "Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting," Energy, Elsevier, vol. 298(C).
  62. 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.
  63. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Gong, Jue & Li, Zhao-Chen & Zhu, You, 2024. "Who dominate the information flowing between innovative and traditional financial assets? A multiscale entropy-based approach," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 329-358.
  64. 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.
  65. Hassan Oukhouya & Aziz Lmakri & Mohamed El Yahyaoui & Raby Guerbaz & Said El Melhaoui & Moustapha Faizi & Khalid El Himdi, 2025. "Predictive modeling for the Moroccan financial market: a nonlinear time series and deep learning approach," Future Business Journal, Springer, vol. 11(1), pages 1-19, December.
  66. 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.
  67. Barua, Ronil & Sharma, Anil K., 2023. "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, vol. 58(PC).
  68. 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.
  69. Muhammad Arslan & Ahmed Imran Hunjra & Wajid Shakeel Ahmed & Younes Ben Zaied, 2024. "Forecasting multi‐frequency intraday exchange rates using deep learning models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1338-1355, August.
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  71. 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.
  72. 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.
  73. 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.
  74. 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.
  75. 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.
  76. 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).
  77. Danijel Jevtic & Romain Deleze & Joerg Osterrieder, 2022. "AI for trading strategies," Papers 2208.07168, arXiv.org.
  78. Wang, Gang-Jin & Chen, Yan & Zhu, You & Xie, Chi, 2024. "Systemic risk prediction using machine learning: Does network connectedness help prediction?," International Review of Financial Analysis, Elsevier, vol. 93(C).
  79. 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.
  80. 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.
  81. Zakaria Boulanouar & Ghassane Benrhmach & Rihab Grassa & Sonia Abdennadher & Mariam Aldhaheri, 2024. "Exploring the predictive power of artificial neural networks in linking global Islamic indices with a local Islamic index," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
  82. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
  83. Lin, Yu & Dai, Dongsheng & Yu, Yuanyuan & Li, Zhaofeng & Huang, Wenhui & Zhao, Liangkai & Xing, Haiyang, 2025. "Forecasting natural gas prices using a novel hybrid model: Comparative study of different sliding windows," Energy, Elsevier, vol. 329(C).
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