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A new multivariate decomposition-ensemble approach with denoised neighborhood rough set for stock price forecasting over time-series information system

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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.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon resonable request.

References

  1. 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

    Article  MATH  Google Scholar 

  2. 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

    Article  MATH  Google Scholar 

  3. 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

    Article  MATH  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  MATH  Google Scholar 

  6. 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

    Article  MATH  Google Scholar 

  7. 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

    Article  MATH  Google Scholar 

  8. 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

    Article  MATH  Google Scholar 

  9. 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

    Article  MATH  Google Scholar 

  10. 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

  11. 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

    Article  MATH  Google Scholar 

  12. 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

    Article  MATH  Google Scholar 

  13. Pai P, Lin C (2005) A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33:497–505

    Article  MATH  Google Scholar 

  14. 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

    Article  MATH  Google Scholar 

  15. 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

    Article  MATH  Google Scholar 

  16. 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

    MATH  Google Scholar 

  17. 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

    MATH  Google Scholar 

  18. 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

  19. 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

    Article  MATH  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    Article  MATH  Google Scholar 

  22. 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

    Article  MATH  Google Scholar 

  23. 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

    Article  MATH  Google Scholar 

  24. 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

    Article  MATH  Google Scholar 

  25. 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

    Article  MATH  Google Scholar 

  26. Sun S, Wei Y, Tsui K, Wang S (2019) Forecasting tourist arrivals with machine learning and internet search index. Tour Manag 70:1–10

    Article  MATH  Google Scholar 

  27. 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

    Article  MATH  Google Scholar 

  28. 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

    Article  MathSciNet  MATH  Google Scholar 

  29. 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

    Article  MATH  Google Scholar 

  30. 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

    Article  Google Scholar 

  31. 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

    Article  Google Scholar 

  32. 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

    Article  MATH  Google Scholar 

  33. Kumar S, Kumar V, Sarangi S, Singh OP (2023) Gearbox fault diagnosis: A higher order moments approach. Measurement 210:112489

    Article  MATH  Google Scholar 

  34. 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

    Article  MATH  Google Scholar 

  35. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356

    Article  MATH  Google Scholar 

  36. 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

    Article  MATH  Google Scholar 

  37. 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

    Article  MATH  Google Scholar 

  38. 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

    Article  MATH  Google Scholar 

  39. 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

    Article  MATH  Google Scholar 

  40. 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

    Article  MATH  Google Scholar 

  41. 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

    Article  MATH  Google Scholar 

  42. 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

    Article  MATH  Google Scholar 

  43. Xu F, Cai M, Li Q, Wang H, Fujita H (2024) Shared neighbors rough set model and neighborhood classifiers. Expert Syst Appl 244:122965

    Article  MATH  Google Scholar 

  44. 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

    Article  MATH  Google Scholar 

  45. 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

    Article  MathSciNet  MATH  Google Scholar 

  46. 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

  47. 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

    Article  MATH  Google Scholar 

  48. 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

    Article  MathSciNet  MATH  Google Scholar 

  49. Yao Y (2008) Probabilistic rough set approximations. Int J Approx Reason 49:255–271

    Article  MATH  Google Scholar 

  50. Zhang J, Li T, Chen H (2014) Composite rough sets for dynamic data mining. Inf Sci 257:81–100

    Article  MathSciNet  MATH  Google Scholar 

  51. Theerens A, Cornelis C (2023) Fuzzy rough sets based on fuzzy quantification. Fuzzy Sets and Syst 473:108704

    Article  MathSciNet  MATH  Google Scholar 

  52. Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178:3577–3594

    Article  MathSciNet  MATH  Google Scholar 

  53. Meng D, Zhang X, Qin K (2011) Soft rough fuzzy sets and soft fuzzy rough sets. Comput & Math Appl 62:4635–4645

    Article  MathSciNet  MATH  Google Scholar 

  54. Du WS, Hu BQ (2016) Dominance-based rough set approach to incomplete ordered information systems. Inf Sci 346–347:106–129

    Article  MathSciNet  MATH  Google Scholar 

  55. 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

    Article  MATH  Google Scholar 

  56. Xu W, Yuan K, Li WL (2022) Dynamic updating approximations of local generalized multigranulation neighborhood rough set. Appl Intell 52:9148–9173

    Article  MATH  Google Scholar 

  57. Xu F, Cai M, Li Q, Wang H, Fujita H (2024) Shared neighbors rough set model and neighborhood classifiers. Expert Syst Appl 244:122965

    Article  MATH  Google Scholar 

  58. Ye J, Zhan J, Ding W, Fujita H (2021) A novel fuzzy rough set model with fuzzy neighborhood operators. Inf Sci 544:266–297

    Article  MathSciNet  MATH  Google Scholar 

  59. 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

    Article  MATH  Google Scholar 

  60. 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

    Article  MATH  Google Scholar 

  61. Yang X, Chen H, Li T, Luo C (2022) A noise-aware fuzzy rough set approach for feature selection. Knowl-Based Syst 250:109092

    Article  MATH  Google Scholar 

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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).

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Contributions

Juncheng Bai: Writing-Original Draft. Bingzhen Sun: Validation, Funding acquisition. Yuqi Guo: Investigation, Validation. Xiaoli Chu: Supervision, Formal analysis.

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Correspondence to Bingzhen Sun or Xiaoli Chu.

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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.

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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.

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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

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