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A two-stage model for stock price prediction based on variational mode decomposition and ensemble machine learning method

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Abstract

Accurate stock price prediction is critical for investment decisions in the stock market. To improve the performance of stock price prediction, this paper proposes a novel two-stage prediction model that consists of a decomposition algorithm, a nonlinear ensemble strategy, and three individual machine learning models. Specifically, in the first stage, the stock price time series is decomposed into a finite number of sub-series by variational mode decomposition (VMD). Subsequently, three individual machine learning models, namely support vector machine regression (SVR), extreme learning machine (ELM), and deep neural network (DNN), are separately employed to predict decomposed sub-series, and then the obtained sub-series predictions of each individual prediction model are aggregated to generate the preliminary stock price predictions. In the second stage, an ELM-based nonlinear ensemble strategy is employed to combine preliminary stock price predictions. To verify the effectiveness of the proposed two-stage model, it is compared with a total of fourteen models in terms of accuracy evaluation, improvement percentage comparison, and statistical test. The empirical results demonstrate that the proposed two-stage model can obtain better performance than other competitor models.

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Data Availability

The data of this study are from Wind database.

References

  • Abdoos AA (2016) A new intelligent method based on combination of vmd and elm for short term wind power forecasting. Neurocomputing 203:111–120

    Article  Google Scholar 

  • Babikir A, Mwambi H (2016) Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods. Empir Econ 51(4):1541–1556

    Article  Google Scholar 

  • Bisoi R, Dash P, Mishra S (2019) Modes decomposition method in fusion with robust random vector functional link network for crude oil price forecasting. Appl Soft Comput 80:475–493

    Article  Google Scholar 

  • Bisoi R, Dash P, Parida A (2019) Hybrid variational mode decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis. Appl Soft Comput 74:652–678

    Article  Google Scholar 

  • Bollerslev T (1986) Generalized autoregressive conditional heteroskedaticicty. J Econometrics 52:307–327

    Article  MathSciNet  Google Scholar 

  • Box G, Jenkins G (1994) Time series analysis: forecasting and control. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Bustos O, Pomares-Quimbaya A (2020) Stock market movement forecast: a systematic review. Expert Syst Sppl 5:156

    Google Scholar 

  • Champernowne D (1948) Sampling theory applied to autoregressive schemes. JR Stat Soc 10:204–231

    MathSciNet  Google Scholar 

  • Chen MR, Zeng GQ, Lu KD et al (2019) A two-layer nonlinear combination method for short-term wind speed prediction based on elm, enn, and lstm. IEEE Internet Things 6(4):6997–7010

    Article  Google Scholar 

  • Cheng CH, Wei LY (2014) A novel time-series model based on empirical mode decomposition for forecasting taiex. Econ Model 36:136–141

    Article  Google Scholar 

  • Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13(3):253–263

    Google Scholar 

  • Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

    Article  MathSciNet  Google Scholar 

  • Du P, Wang J, Yang W et al (2019) Container throughput forecasting using a novel hybrid learning method with error correction strategy. Knowl-Based Syst 182(104):853

    Google Scholar 

  • Fernández C, Salinas L, Torres CE (2018) A meta extreme learning machine method for forecasting financial time series. Appl Intell 49(2):532–554

    Article  Google Scholar 

  • Fijani E, Barzegar R, Deo R et al (2019) Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters. Sci Total Environ 648:839–853

    Article  Google Scholar 

  • Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654–669

    Article  MathSciNet  Google Scholar 

  • Guo F, Polak JW, Krishnan R (2018) Predictor fusion for short-term traffic forecasting. Transp Res Part C-Emer 92:90–100

    Article  Google Scholar 

  • Guo Y, Han S, Shen C et al (2018) An adaptive svr for high-frequency stock price forecasting. IEEE Access 6:11397–11404

    Article  Google Scholar 

  • Heaton J, Polson N (2017) Deep learning for finance: deep portfolios. Appl Stoch Model Bus 33(1):3–12

    Article  MathSciNet  Google Scholar 

  • Houssein EH, Dirar M, Abualigah L et al (2021) An efficient equilibrium optimizer with support vector regression for stock market prediction. Neural Comput Appl 34(4):3165–3200

    Article  Google Scholar 

  • Hsieh TJ, Hsiao HF, Yeh WC (2011) Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm. Appl Soft Comput 11(2):2510–2525

    Article  Google Scholar 

  • Hu YL, Chen L (2018) A nonlinear hybrid wind speed forecasting model using lstm network, hysteretic elm and differential evolution algorithm. Energ Convers Manag 173:123–142

    Article  Google Scholar 

  • Huang GB, Siew C (2012) Extreme learning machine: Rbf network case. In: Control, automation, robotics and vision conference, 2004. ICARCV 2004 8th IEEE

  • Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  • Jiang WW (2021) Applications of deep learning in stock market prediction: recent progress. Expert Syst Appl 5:184

    MathSciNet  Google Scholar 

  • Jin Z, Yang Y, Liu Y (2019) Stock closing price prediction based on sentiment analysis and lstm. Neural Comput Appl 32(13):9713–9729

    Article  Google Scholar 

  • Kao LJ, Chiu CC, Lu CJ et al (2013) Integration of nonlinear independent component analysis and support vector regression for stock price forecasting. Neurocomputing 99:534–542

    Article  Google Scholar 

  • Kazem A, Sharifi E, Hussain FK et al (2013) Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl Soft Comput 13(2):947–958

    Article  Google Scholar 

  • Khuwaja P, Khowaja S, Khoso I et al (2020) Prediction of stock movement using phase space reconstruction and extreme learning machines. J Exp Theor Artif 32(1):59–79

    Article  Google Scholar 

  • Lahmiri S (2016) Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques. Phys A 444:388–396

    Article  MathSciNet  Google Scholar 

  • Lahmiri S (2018) Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Appl Math Comput 320:444–451

    MathSciNet  Google Scholar 

  • Li X, Xie H, Wang R et al (2014) Empirical analysis: stock market prediction via extreme learning machine. Neural Comput Appl 27(1):67–78

    Article  Google Scholar 

  • Liu H, Mi (2018) Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, lstm network and elm. Energ Convers Manag 159:842–854

    Article  Google Scholar 

  • Liu H, Mi X, Li Y et al (2019) Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional gated recurrent unit network and support vector regression. Renew Energ 143:842–854

    Article  Google Scholar 

  • Liu Y, Yang C, Huang K et al (2020) Non-ferrous metals price forecasting based on variational mode decomposition and lstm network. Knowl-Based Syst 188(105):006

    Google Scholar 

  • Ma C, Yan S (2022) Deep learning in the Chinese stock market: the role of technical indicators. Financ Res Lett 5: 49

  • Naik N, Mohan BR (2019) Intraday stock prediction based on deep neural network. Natl Acad Sci Lett 43(3):241–246

    Article  Google Scholar 

  • Niu H, Xu K (2020) A hybrid model combining variational mode decomposition and an attention-gru network for stock price index forecasting. Math Biosci Eng 17(6):7151–7166

    Article  MathSciNet  Google Scholar 

  • Niu H, Xu K, Wang W (2020) A hybrid stock price index forecasting model based on variational mode decomposition and lstm network. Appl Intell 50:4296–4309

    Article  Google Scholar 

  • Novykov V, Bilson C, Gepp A et al (2022) Empirical validation of elm trained neural networks for financial modelling. Neural Comput Appl 2:1–25

    Google Scholar 

  • Peng T, Zhou J, Zhang C et al (2017) Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and adaboost-extreme learning machine. Energ Convers Manag 153:589–602

    Article  Google Scholar 

  • Rahimi ZH, Khashei M (2018) A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting. Comput Ind Eng 118:44–53

    Article  Google Scholar 

  • Rahimi ZH, Khashei M (2018) A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting. Comput Ind Eng 118:44–53

    Article  Google Scholar 

  • Rather AM, Agarwal A, Sastry VN (2015) Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst Appl 42(6):3234–3241

    Article  Google Scholar 

  • Raza MQ, Nadarajah M, Ekanayake C (2017) Demand forecast of pv integrated bioclimatic buildings using ensemble framework. Appl Energ 208:1626–1638

    Article  Google Scholar 

  • Saba T, Rehman A, AlGhamdi JS (2017) Weather forecasting based on hybrid neural model. Appl Water Sci 7(7):3869–3874

    Article  Google Scholar 

  • Shen S, Li G, Song H (2008) An assessment of combining tourism demand forecasts over different time horizons. J Travel Res 47(2):197–207

    Article  Google Scholar 

  • Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Vapnik VN (2000) The nature of statistical learning theory. Springer, Berlin

    Book  Google Scholar 

  • Wang FK, Du T (2014) Implementing support vector regression with differential evolution to forecast motherboard shipments. Expert Syst Appl 41(8):3850–3855

    Article  Google Scholar 

  • Wang J, Wang J (2017) Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Netw 90:8–20

    Article  Google Scholar 

  • Wang JJ, Wang JZ, Zhang ZG et al (2012) Stock index forecasting based on a hybrid model. Omega 40(6):758–766

    Article  Google Scholar 

  • Wang L, Wang Z, Qu H et al (2018) Optimal forecast combination based on neural networks for time series forecasting. Appl Soft Comput 66:1–17

    Article  Google Scholar 

  • Wei LY (2016) A hybrid anfis model based on empirical mode decomposition for stock time series forecasting. Appl Soft Comput 42:368–376

    Article  Google Scholar 

  • Wu Y, Gao J (2018) Application of support vector neural network with variational mode decomposition for exchange rate forecasting. Soft Comput 23(16):6995–7004

    Article  Google Scholar 

  • Xiao J, Zhu X, Huang C et al (2019) A new approach for stock price analysis and prediction based on ssa and svm. Int J Inf Tech Decis 18(01):287–310

    Article  Google Scholar 

  • Xiao L, Wang J, Dong Y et al (2015) Combined forecasting models for wind energy forecasting: a case study in China. Renew Sust Energ Rev 44:271–288

    Article  Google Scholar 

  • Xu Z, Zhang J, Wang J et al (2020) Prediction research of financial time series based on deep learning. Soft Comput 24(11):8295–8312

    Article  Google Scholar 

  • Yeh CY, Huang CW, Lee SJ (2011) A multiple-kernel support vector regression approach for stock market price forecasting. Expert Syst Appl 38(3):2177–2186

    Article  Google Scholar 

  • Zhang GP (2003) Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50:159–175

    Article  Google Scholar 

  • Zhang W, Qu Z, Zhang K et al (2017) A combined model based on ceemdan and modified flower pollination algorithm for wind speed forecasting. Energ Convers Manag 136:439–451

  • Zhao F, Zeng GQ, Lu KD (2020) Enlstm-wpeo: short-term traffic flow prediction by ensemble lstm, nnct weight integration, and population extremal optimization. IEEE Trans Veh Technol 69(1):101–113

    Article  Google Scholar 

  • Zheng W, Shu H, Tang H et al (2019) Spectra data classification with kernel extreme learning machine. Chemometr Intell Lab 192(103):815

    Google Scholar 

  • Zhou F, Hm Z, Yang Z et al (2019) Emd2fnn: a strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction. Expert Syst Appl 115:136–151

    Article  Google Scholar 

  • Zhou Y, Zhu Z (2019) A hybrid method for noise suppression using variational mode decomposition and singular spectrum analysis. J Appl Genet 161:105–115

    Google Scholar 

  • Zhou Z, Si G, Zheng K et al (2019) Cmbcf: a cloud model based hybrid method for combining forecast. Appl Soft Comput 85(105):766

    Google Scholar 

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Authors and Affiliations

  1. School of Management and Engineering, Capital University of Economics and Business, Beijing, 100070, China

    Jun Zhang & Xuedong Chen

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  1. Jun Zhang
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  2. Xuedong Chen
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JZ contributed to conceptualization, writing—review and editing, and supervision. XC contributed to conceptualization, methodology, software, and writing—original draft.

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Correspondence to Xuedong Chen.

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Zhang, J., Chen, X. A two-stage model for stock price prediction based on variational mode decomposition and ensemble machine learning method. Soft Comput 28, 2385–2408 (2024). https://doi.org/10.1007/s00500-023-08441-0

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