Time Series Forecasting Enhanced by Integrating GRU and N-BEATS

Time Series Forecasting Enhanced by Integrating GRU and N-BEATS

Автор: Milind Kolambe, Sandhya Arora

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 1 vol.17, 2025 года.

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Accurate stock price prediction is crucial for financial markets, where investors and analysts forecast future prices to support informed decision-making. In this study, various methods for integrating two advanced time series prediction models, Gated Recurrent Unit (GRU) and Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), are explored to enhance stock price prediction accuracy. GRU is recognized for its ability to capture temporal dependencies in sequential data, while N-BEATS is known for handling complex trends and seasonality components. Several integration techniques, including feature fusion, residual learning, Ensemble learning and hybrid modeling, are proposed to leverage the strengths of both models and improve forecasting performance. These methods are evaluated on datasets of ten stocks from the S&P 500, with some exhibiting strong seasonal or cyclic patterns and others lacking such characteristics. Results demonstrate that the integrated models consistently outperform individual models. Feature selection, including the integration of technical indicators, is employed during data processing to further improve prediction accuracy.

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Time Series, Stock Price Prediction, N-BEATS Model, GRU Model, Financial Indicators, Information Engineering

Короткий адрес: https://sciup.org/15019663

IDR: 15019663   |   DOI: 10.5815/ijieeb.2025.01.07

Список литературы Time Series Forecasting Enhanced by Integrating GRU and N-BEATS

  • Pearce, Douglas K. "Challenges to the concept of stock market efficiency." Economic Review 72, no. 8 (1987): 16-33.
  • Han, Chenyu, and Xiaoyu Fu. "Challenge and opportunity: deep learning-based stock price prediction by using Bi-directional LSTM model." Frontiers in Business, Economics and Management 8, no. 2 (2023): 51-54.
  • Cao, Hongduo, Tiantian Lin, Ying Li, and Hanyu Zhang. "Stock price pattern prediction based on complex network and machine learning." Complexity 2019, no. 1 (2019): 4132485.
  • Zhang, Liheng, Charu Aggarwal, and Guo-Jun Qi. "Stock price prediction via discovering multi-frequency trading patterns." In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 2141-2149. 2017.
  • Han, Zhongyang, Jun Zhao, Henry Leung, King Fai Ma, and Wei Wang. "A review of deep learning models for time series prediction." IEEE Sensors Journal 21, no. 6 (2019): 7833-7848.
  • Tang, Yajiao, Zhenyu Song, Yulin Zhu, Huaiyu Yuan, Maozhang Hou, Junkai Ji, Cheng Tang, and Jianqiang Li. "A survey on machine learning models for financial time series forecasting." Neurocomputing 512 (2022): 363-380.
  • Rahimzad, Maryam, Alireza Moghaddam Nia, Hosam Zolfonoon, Jaber Soltani, Ali Danandeh Mehr, and Hyun-Han Kwon. "Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting." Water Resources Management 35, no. 12 (2021): 4167-4187.
  • Liu, Yang. "Novel volatility forecasting using deep learning–long short term memory recurrent neural networks." Expert Systems with Applications 132 (2019): 99-109.
  • Cortez, Bitzel, Berny Carrera, Young-Jin Kim, and Jae-Yoon Jung. "An architecture for emergency event prediction using LSTM recurrent neural networks." Expert Systems with Applications 97 (2018): 315-324.
  • Cahuantzi, Roberto, Xinye Chen, and Stefan Güttel. "A comparison of LSTM and GRU networks for learning symbolic sequences." In Science and Information Conference, pp. 771-785. Cham: Springer Nature Switzerland, 2023.
  • Yamak, Peter T., Li Yujian, and Pius K. Gadosey. "A comparison between arima, lstm, and gru for time series forecasting." In Proceedings of the 2019 2nd international conference on algorithms, computing and artificial intelligence, pp. 49-55. 2019.
  • Khandelwal, Shubham, Benjamin Lecouteux, and Laurent Besacier. "Comparing GRU and LSTM for automatic speech recognition." PhD diss., LIG, 2016.
  • Yang, Shudong, Xueying Yu, and Ying Zhou. "Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example." In 2020 International workshop on electronic communication and artificial intelligence (IWECAI), pp. 98-101. IEEE, 2020.
  • Mateus, Balduíno César, Mateus Mendes, José Torres Farinha, Rui Assis, and António Marques Cardoso. "Comparing LSTM and GRU models to predict the condition of a pulp paper press." Energies 14, no. 21 (2021): 6958.
  • Yuan, Jing, and Ying Tian. "An intelligent fault diagnosis method using GRU neural network towards sequential data in dynamic processes." Processes 7, no. 3 (2019): 152.
  • Zargar, S. "Introduction to sequence learning models: RNN, LSTM, GRU." Department of Mechanical and Aerospace Engineering, North Carolina State University (2021).
  • Nosouhian, Shiva, Fereshteh Nosouhian, and Abbas Kazemi Khoshouei. "A review of recurrent neural network architecture for sequence learning: Comparison between LSTM and GRU." (2021).
  • Li, Dan, Guangfan Sun, Shuwei Miao, Ying zhong Gu, Yuan hang Zhang, and Shuai He. "A short-term electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence." International Journal of Electrical Power & Energy Systems 137 (2022): 107627.
  • Zheng, Wendong, and Gang Chen. "An accurate GRU-based power time-series prediction approach with selective state updating and stochastic optimization." IEEE Transactions on Cybernetics 52, no. 12 (2021): 13902-13914.
  • Rehmer, Alexander, and Andreas Kroll. "On the vanishing and exploding gradient problem in Gated Recurrent Units." IFAC-PapersOnLine 53, no. 2 (2020): 1243-1248.
  • Oreshkin, Boris N., Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. "N-BEATS: Neural basis expansion analysis for interpretable time series forecasting." arXiv preprint arXiv:1905.10437 (2019).
  • Ma, Xiaobing, Bingxin Yan, Han Wang, and Haitao Liao. "Decision-level machinery fault prognosis using N-BEATS-based degradation feature prediction and reconstruction." Mechanical Systems and Signal Processing 198 (2023): 110435.
  • Tan, Chang, Xiang Yu, Lihua Lu, and Lisen Zhao. "An Effective N-BEATS Network Model for Short Term Load Forecasting." In International Conference on 6GN for Future Wireless Networks, pp. 229-243. Cham: Springer Nature Switzerland, 2023.
  • Becerra-Rico, Josue, Marco A. Aceves-Fernández, Karen Esquivel-Escalante, and Jesús Carlos Pedraza-Ortega. "Airborne particle pollution predictive model using Gated Recurrent Unit (GRU) deep neural networks." Earth Science Informatics 13 (2020): 821-834.
  • Michańków, Jakub, Paweł Sakowski, and Robert Ślepaczuk. "LSTM in algorithmic investment strategies on BTC and S&P500 index." Sensors 22, no. 3 (2022): 917.
  • Oliver-Muncharaz, Javier. "Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks." Finance, Markets and Valuation 6, no. 2 (2020): 137-148.
  • Roszyk, Natalia, and Robert Ślepaczuk. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models." arXiv preprint arXiv:2407.16780 (2024).
  • Bhandari, Hum Nath, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal, and Rajendra KC Khatri. "Predicting stock market index using LSTM." Machine Learning with Applications 9 (2022): 100320.
  • Zou, Zhichao, and Zihao Qu. "Using lstm in stock prediction and quantitative trading." CS230: Deep learning, winter (2020): 1-6.
  • Kim, Sangyeon, and Myungjoo Kang. "Financial series prediction using Attention LSTM." arXiv preprint arXiv:1902.10877 (2019).
  • Bergström, Carl, and Oscar Hjelm. "Impact of time steps on stock market prediction with lstm." (2019).
  • Eapen, Jithin, Doina Bein, and Abhishek Verma. "Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction." In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), pp. 0264-0270. IEEE, 2019.
  • Feng, Zexu. "Research on the S&P 500 Index Based on LSTM and GRU." Highlights in Business, Economics and Management 21 (2023): 38-48.
  • Song, Hyunsun, and Hyunjun Choi. "Forecasting stock market indices using the recurrent neural network based hybrid models: CNN-LSTM, GRU-CNN, and ensemble models." Applied Sciences 13, no. 7 (2023): 4644.
  • Althelaya, Khaled A., El-Sayed M. El-Alfy, and Salahadin Mohammed. "Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU)." In 2018 21st Saudi Computer Society National Computer Conference (NCC), pp. 1-7. IEEE, 2018.
  • Eapen, Jithin, Abhishek Verma, and Doina Bein. "Improved big data stock index prediction using deep learning with CNN and GRU." International Journal of Big Data Intelligence 7, no. 4 (2020): 202-210.
  • Qi, Chenyang, Jiaying Ren, and Jin Su. "GRU neural network based on CEEMDAN–wavelet for stock price prediction." Applied Sciences 13, no. 12 (2023): 7104.
  • Sethia, Akhil, and Purva Raut. "Application of LSTM, GRU and ICA for stock price prediction." In Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018, Volume 2, pp. 479-487. Springer Singapore, 2019.
  • Munkhdalai, Lkhagvadorj, Meijing Li, Nipon Theera-Umpon, Sansanee Auephanwiriyakul, and Keun Ho Ryu. "VAR-GRU: A hybrid model for multivariate financial time series prediction." In Asian Conference on Intelligent Information and Database Systems, pp. 322-332. Cham: Springer International Publishing, 2020.
  • Oreshkin, B. N., Dudek, G., Pełka, P., & Turkina, E. (2021). N-BEATS neural network for mid-term electricity load forecasting. Applied Energy, 293, 116918.
  • Sbrana, A., Rossi, A. L. D., & Naldi, M. C. (2020, December). N-BEATS-RNN: Deep learning for time series forecasting. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 765-768). IEEE.
  • Puszkarski, B., Hryniów, K., & Sarwas, G. (2021, September). N-beats for heart dysfunction classification. In 2021 Computing in Cardiology (CinC) (Vol. 48, pp. 1-4). IEEE.
  • Wang, X., Li, C., Yi, C., Xu, X., Wang, J., & Zhang, Y. (2022). EcoForecast: An interpretable data-driven approach for short-term macroeconomic forecasting using N-BEATS neural network. Engineering Applications of Artificial Intelligence, 114, 105072.
  • Hyndman, Rob J. "The interaction between trend and seasonality." International Journal of Forecasting 20, no. 4 (2004): 561-563.
  • Pezzulli, S., D. B. Stephenson, and A. Hannachi. "The variability of seasonality." Journal of Climate 18, no. 1 (2005): 71-88.
  • Koopman, Siem Jan, and Kai Ming Lee. "Seasonality with trend and cycle interactions in unobserved components models." Journal of the Royal Statistical Society Series C: Applied Statistics 58, no. 4 (2009): 427-448.
  • Verbesselt, Jan, Rob Hyndman, Glenn Newnham, and Darius Culvenor. "Detecting trend and seasonal changes in satellite image time series." Remote sensing of Environment 114, no. 1 (2010): 106-115.
  • Di, Xinjie. "Stock trend prediction with technical indicators using SVM." Independent Work Report, Standford: Leland Stanford Junior University, USA (2014).
  • Fang, Jiali, Yafeng Qin, and Ben Jacobsen. "Technical market indicators: An overview." Journal of behavioral and experimental finance 4 (2014): 25-56.
  • Chen, Chien-Ming, Yuxiao Gong, and Jimmy Ming-Tai Wu. "Impact of technical indicators and leading indicators on stock trends on the internet of things." Wireless Communications and Mobile Computing 2022, no. 1 (2022): 9916310.
  • Peachavanish, Ratchata. "Stock selection and trading based on cluster analysis of trend and momentum indicators." In Proceedings of the international multiconference of engineers and computer scientists, vol. 1, pp. 317-321. IMECS, 2016.
  • Blau, William. Momentum, direction, and divergence. Vol. 5. John Wiley & Sons, 1995.
  • Lewellen, Jonathan. "Momentum and autocorrelation in stock returns." The Review of Financial Studies 15, no. 2 (2002): 533-564.
  • Chun, Dohyun, Hoon Cho, and Doojin Ryu. "Economic indicators and stock market volatility in an emerging economy." Economic Systems 44, no. 2 (2020): 100788.
  • Schwert, G. William. "Stock volatility during the recent financial crisis." European Financial Management 17, no. 5 (2011): 789-805.
  • Otchere, Daniel Asante, Tarek Omar Arbi Ganat, Jude Oghenerurie Ojero, Bennet Nii Tackie-Otoo, and Mohamed Yassir Taki. "Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions." Journal of Petroleum Science and Engineering 208 (2022): 109244.
  • Bai, Ting, Deren Li, Kaimin Sun, Yepei Chen, and Wenzhuo Li. "Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion." Remote Sensing 8, no. 9 (2016): 715.
  • Meng, Tong, Xuyang Jing, Zheng Yan, and Witold Pedrycz. "A survey on machine learning for data fusion." Information Fusion 57 (2020): 115-129.
  • He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep residual learning for image recognition." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
  • Shafiq, Muhammad, and Zhaoquan Gu. "Deep residual learning for image recognition: A survey." Applied Sciences 12, no. 18 (2022): 8972.
  • Sill, Joseph, Gábor Takács, Lester Mackey, and David Lin. "Feature-weighted linear stacking." arXiv preprint arXiv:0911.0460 (2009).
  • Coscrato, Victor, Marco Henrique de Almeida Inacio, and Rafael Izbicki. "The NN-Stacking: Feature weighted linear stacking through neural networks." Neurocomputing 399 (2020): 141-152.
  • Guo, Fusen, Huadong Mo, Jianzhang Wu, Lei Pan, Hailing Zhou, Zhibo Zhang, Lin Li, and Fengling Huang. "A Hybrid Stacking Model for Enhanced Short-Term Load Forecasting." Electronics 13, no. 14 (2024): 2719.
  • Kardani, Navid, Annan Zhou, Majidreza Nazem, and Shui-Long Shen. "Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data." Journal of Rock Mechanics and Geotechnical Engineering 13, no. 1 (2021): 188-201.
  • Chung, Junyoung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3555 (2014).
  • Yu, Yong, Xiaosheng Si, Changhua Hu, and Jianxun Zhang. "A review of recurrent neural networks: LSTM cells and network architectures." Neural computation 31, no. 7 (2019): 1235-1270.
  • Cahuantzi, Roberto, Xinye Chen, and Stefan Güttel. "A comparison of LSTM and GRU networks for learning symbolic sequences." In Science and Information Conference, pp. 771-785. Cham: Springer Nature Switzerland, 2023.
  • Yiğit, Gülsüm, and Mehmet Fatih Amasyali. "Simple but effective GRU variants." In 2021 international conference on
  • Gao, Shuai, Yuefei Huang, Shuo Zhang, Jingcheng Han, Guangqian Wang, Meixin Zhang, and Qingsheng Lin. "Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation." Journal of Hydrology 589 (2020): 125188.
  • Choe, Do-Eun, Hyoung-Chul Kim, and Moo-Hyun Kim. "Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades." Renewable Energy 174 (2021): 218-235.
  • Zhang, Yue, Zimo Zhou, Jesse Van Griensven Thé, Simon X. Yang, and Bahram Gharabaghi. "Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing." Water 15, no. 22 (2023): 3982.
  • Hassanian, Reza, Ásdís Helgadóttir, and Morris Riedel. "Deep learning forecasts a strained turbulent flow velocity field in temporal lagrangian framework: Comparison of LSTM and GRU." Fluids 7, no. 11 (2022): 344.
  • Shewalkar, Apeksha, Deepika Nyavanandi, and Simone A. Ludwig. "Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU." Journal of Artificial Intelligence and Soft Computing Research 9, no. 4 (2019): 235-245.
  • Yamak, Peter T., Li Yujian, and Pius K. Gadosey. "A comparison between arima, lstm, and gru for time series forecasting." In Proceedings of the 2019 2nd international conference on algorithms, computing and artificial intelligence, pp. 49-55. 2019.
  • Gao, Ya, Rong Wang, and Enmin Zhou. "Stock prediction based on optimized LSTM and GRU models." Scientific Programming 2021, no. 1 (2021): 4055281.
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