Abstract
The uncertainty of the stock market is the foundation for investors to obtain returns. Driven by interests, stock price forecasting has become a research hotspot. However, as the high latitude, highly volatile, and noisy, forecasting the stock prices has become a highly challenging task. The existing stock price forecasting methods only study low latitude data, which is unable to reflect the cumulative effect of multiple factors on stock price. To effectively address the high latitude, high volatility, and noise of stock price, a time-series information system (TSIS) forecasting approach for stock price is proposed. Aiming at dynamically depicting the real-world decision-making scenarios from a finer granularity, the TSIS is constructed based on the information systems. Then, a denoised neighborhood rough set (DNRS) model based on the TSIS is proposed by local density factor to achieve the purpose of feature selection, which can weaken the impact of noise on sample data. Subsequently, the multivariate empirical mode decomposition (MEMD) and multivariate kernel extreme learning machine (MKELM) are employed to decompose and forecast. Finally, the proposed TSIS forecasting approach is applied to stock price. Experimental results show that the TSIS forecasting approach for stock price has excellent performance and can be provided in the quantitative trading of stock market.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The data that support the findings of this study are available from the corresponding author upon resonable request.
References
Bai J, Guo J, Sun B, Guo Y, Bao Q, Xiao X (2023) Intelligent forecasting model of stock price using neighborhood rough set and multivariate empirical mode decomposition. Eng Appl Artif Intell 122:106106
Yao Y, Zhang ZY, Zhao Y (2023) Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks. Appl Soft Comput 142:110356
Wang J, Hu Y, Jiang T, Tan J, Li Q (2023) Essential tensor learning for multimodal information-driven stock movement prediction. Knowl-Based Syst 262:110262
Deng C, Huang Y, Hasan N, Bao Y (2022) Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition. Inf Sci 607:297–321
Jianming Zhan YQWD, Huang Xianfeng (2024) A fuzzy C-means clustering-based hybrid multivariate time series prediction framework with feature selection. IEEE Trans Fuzzy Syst 32:4270–4284
Kang H, Zong X, Wang J, Chen H (2023) Binary gravity search algorithm and support vector machine for forecasting and trading stock indices. Int Rev Econ Financ 84:507–526
Khodaee P, Esfahanipour A, Taheri HM (2022) Forecasting turning points in stock price by applying a novel hybrid CNN-LSTM-ResNet model fed by 2D segmented images. Eng Appl Artif Intell 116:105464
Zhu CL, Ma XL, Ding WP, Zhan JM (2024) Long-term time series forecasting with multi-linear trend fuzzy information granules for LSTM in a periodic framework. IEEE Trans Fuzzy Syst 32:322–336
Tavares THBC, Ferreira BP, Mendes EMAM (2022) Fuzzy time series model based on red-black trees for stock index forecasting. Appl Soft Comput 127:109323
Xu S, Chan HK, Zhang T (2019) Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach. Trans Res Part E: Logist Trans Revi 122:169–180
Zolfaghari M, Gholami S (2021) A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction. Expert Syst Appl 182:115149
Wang L, Ma F, Liu J, Yang L (2020) Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. Int J Forecast 36:684–694
Pai P, Lin C (2005) A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33:497–505
Rongtao Zhang CZWDJZ, Ma Xueling (2024) GACFCFNN: A new forecasting method combining feature selection methods and feedforward neural networks using genetic algorithms. Inf Sci 669:120566
Zhu CL, Ma XL, Zhang C, Ding WP, Zhan JM (2023) Information granules-based long-term forecasting of time series via BPNN under three-way decision framework. Inf Sci 634:696–715
Sattarhoff C, Lux T (2022) Forecasting the variability of stock index returns with the multifractal random walk model for realized volatilities. Int J Forecast 95:118595
Chenglong Zhu PDUYQWDJZ, Ma Xueling (2024) Long-term multivariate time series forecasting model based on Gaussian fuzzy information granules. IEEE Trans Fuzzy Syst 30:1203–1212
Zhang C, Li J, Huang X, Zhang J, Huang H (2022) Forecasting stock volatility and value at risk based on temporal convolutional networks. Expert Syst Appl 207:117951
Tang H, Dong P, Shi Y (2019) A new approach of integrating piecewise linear representation and weighted support vector machine for forecasting stock turning points. Appl Soft Comput 78:685–696
Wu XJ, Zhan JM, Li TR, Ding WP, Pedrycz W (2024) MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network by fusing modified binary salp swarm algorithm and feature selection. Appl Intell 54:1706–1733
Zhu C, Ma X, Ding W, Zhan J (2024) Long-Term Time series forecasting with multilinear trend fuzzy information granules for LSTM in a periodic framework. IEEE Trans Fuzzy Syst 32:322–336
Xunjin Wu TLWDWP, Zhan Jianming (2024) MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network by fusing modified binary salp swarm algorithm and feature selection. Appl Intell 54:1706–1733
Lin Y, Lin Z, Liao Y, Li Y, Xu J, Yan Y (2022) Forecasting the realized volatility of stock price index: A hybrid model integrating CEEMDAN and LSTM. Expert Syst Appl 206:117736
Xianfeng Huang WDWP, Zhan Jianming (2023) Regret theory-based multivariate fusion prediction system and its application to interest rate estimation in multi-scale information systems. Inf Fusion 99:101860
Rongtao Zhang WDJZ, Ma Xueling (2023) Map-fcrnn: Multi-step ahead prediction model using forecasting correction and rnn model with memory functions. Inf Sci 646:119382
Sun S, Wei Y, Tsui K, Wang S (2019) Forecasting tourist arrivals with machine learning and internet search index. Tour Manag 70:1–10
Xunjin Wu WD, Zhan Jianming (2023) TWC-EL: A multivariate prediction model by the fusion of three-way clustering and ensemble learning. Inf Fusion 100:101966
Xianfeng Huang WDWP, Zhan Jianming (2022) An error correction prediction model based on three-way decision and ensemble learning. Int J Approx Reason 146:21–46
Wang P, Liu J, Tao Z, Chen H (2022) A novel carbon price combination forecasting approach based on multi-source information fusion and hybrid multi-scale decomposition. Eng Appl Artif Intell 114:105172
Mueller PN, Woelfl L, Can S (2023) Bridging the gap between AI and the industry - A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement. Eng Appl Artif Intell 126:106834
Xue L, Wu H, Zheng H, He Z (2023) Control chart pattern recognition for imbalanced data based on multi-feature fusion using convolutional neural network. Comput & Ind Eng 182:109410
Zhao X, Sun B, Geng R (2023) A new distributed decomposition creconstruction censemble learning paradigm for short-term wind power prediction. Journal of Cleaner Production 423:138676
Kumar S, Kumar V, Sarangi S, Singh OP (2023) Gearbox fault diagnosis: A higher order moments approach. Measurement 210:112489
Yu Y, Li H, Sun S, Li Y (2022) PM2.5 concentration forecasting through a novel multi-scale ensemble learning approach considering intercity synergy. Sustain Cities Soc 85:104049
Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356
Zhang RT, Ma XL, Zhan JM, Yao YY (2023) 3WC-D: A feature distribution-based adaptive three-way clustering method. Appl Intell 53:15561–15579
Xu W, Yuan Z, Liu Z (2023) Feature selection for unbalanced distribution hybrid data based on k-nearest neighborhood rough set. IEEE Trans Artif Intell 5:229–243
Sang B, Xu W, Chen H, Li T (2023) Active anti-noise fuzzy dominance rough feature selection using adaptive k-nearest neighbors. IEEE Trans Fuzzy Syst 31:3944–3958
Yuan K, Xu W, Miao D (2024) A local rough set method for feature selection by variable precision composite measure. Appl Soft Comput 155:111450
Wan J, Chen H, Yuan Z, Li T, Yang X, Sang B (2021) A novel hybrid feature selection method considering feature interaction in neighborhood rough set. Knowl-Based Syst 227:107167
Xu W, Yang Y (2023) Matrix-based feature selection approach using conditional entropy for ordered data set with time-evolving features. Knowl-Based Syst 279:110947
Zhan J, Zhang K, Wu W (2021) An investigation on Wu-Leung multi-scale information systems and multi-expert group decision-making. Expert Syst Appl 170:114542
Xu F, Cai M, Li Q, Wang H, Fujita H (2024) Shared neighbors rough set model and neighborhood classifiers. Expert Syst Appl 244:122965
Jin C, Mi J, Li F, Liang M (2022) A novel probabilistic hesitant fuzzy rough set based multi-criteria decision-making method. Inf Sci 608:489–516
Yang X, Li T, Liu D, Fujita H (2020) A multilevel neighborhood sequential decision approach of three-way granular computing. Inf Sci 538:119–141
Yuan Z, Chen H, Xie P, Zhang P, Liu J, Li T (2021) Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions. Appl Soft Comput 107:107353
Kou Y, Lin G, Qian Y, Liao S (2023) A novel multi-label feature selection method with association rules and rough set. Inf Sci 624:299–323
Ye J, Zhan J, Sun B (2021) A three-way decision method based on fuzzy rough set models under incomplete environments. Inf Sci 577:22–48
Yao Y (2008) Probabilistic rough set approximations. Int J Approx Reason 49:255–271
Zhang J, Li T, Chen H (2014) Composite rough sets for dynamic data mining. Inf Sci 257:81–100
Theerens A, Cornelis C (2023) Fuzzy rough sets based on fuzzy quantification. Fuzzy Sets and Syst 473:108704
Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178:3577–3594
Meng D, Zhang X, Qin K (2011) Soft rough fuzzy sets and soft fuzzy rough sets. Comput & Math Appl 62:4635–4645
Du WS, Hu BQ (2016) Dominance-based rough set approach to incomplete ordered information systems. Inf Sci 346–347:106–129
Sun B, Ma W, Qian Y (2017) Multigranulation fuzzy rough set over two universes and its application to decision making. Knowl-Based Syst 123:61–74
Xu W, Yuan K, Li WL (2022) Dynamic updating approximations of local generalized multigranulation neighborhood rough set. Appl Intell 52:9148–9173
Xu F, Cai M, Li Q, Wang H, Fujita H (2024) Shared neighbors rough set model and neighborhood classifiers. Expert Syst Appl 244:122965
Ye J, Zhan J, Ding W, Fujita H (2021) A novel fuzzy rough set model with fuzzy neighborhood operators. Inf Sci 544:266–297
Huang X, Zhan J, Xu Z, Fujita H (2023) A prospect-regret theory-based three-way decision model with intuitionistic fuzzy numbers under incomplete multi-scale decision information systems. Expert Syst Appl 214:119144
Hu M, Tsang EC, Guo Y, Chen D, Xu W (2021) A novel approach to attribute reduction based on weighted neighborhood rough sets. Knowl-Based Syst 220:106908
Yang X, Chen H, Li T, Luo C (2022) A noise-aware fuzzy rough set approach for feature selection. Knowl-Based Syst 250:109092
Acknowledgements
The work was partly supported by the National Natural Science Foundation of China (No. 72071152, 72301082), Shaanxi National Funds for Distinguished Young Scientists, China (No. 2023-JC-JQ-11), the Fundamental Research Funds for the Central Universities (No.ZYTS24049), Guangzhou Key Research and Development Program (No. 202206010101), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110703), Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Project (No. YN2022QN33).
Author information
Authors and Affiliations
Contributions
Juncheng Bai: Writing-Original Draft. Bingzhen Sun: Validation, Funding acquisition. Yuqi Guo: Investigation, Validation. Xiaoli Chu: Supervision, Formal analysis.
Corresponding authors
Ethics declarations
Competing of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical and informed consent for data used
The data used in this study was sourced from the Wind database. As a user of the Wind database, this study obtained the necessary permissions and subscriptions to access and utilize the data. The usage of the data complied with the terms and conditions set by Wind Information Co., Ltd., the provider of the Wind database. These terms and conditions ensure the proper and ethical use of the data, protecting the rights and interests of the data contributors and the integrity of the database.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Bai, J., Sun, B., Guo, Y. et al. A new multivariate decomposition-ensemble approach with denoised neighborhood rough set for stock price forecasting over time-series information system. Appl Intell 55, 284 (2025). https://doi.org/10.1007/s10489-024-06070-0
Accepted:
Published:
DOI: https://doi.org/10.1007/s10489-024-06070-0