메뉴 건너뛰기




Volumn 12, Issue , 2016, Pages 23-27

Intraday stock price forecasting based on variational mode decomposition

Author keywords

Artificial neural networks; Forecasting; Intraday stock price; Particle swarm optimization; Variational mode decomposition

Indexed keywords

COSTS; ELECTRONIC TRADING; FINANCIAL MARKETS; FORECASTING; NEURAL NETWORKS;

EID: 84954156935     PISSN: 18777503     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jocs.2015.11.011     Document Type: Article
Times cited : (90)

References (20)
  • 1
    • 84954119339 scopus 로고    scopus 로고
    • Advanced river flood monitoring, modelling and forecasting
    • Merkuryeva G., et al. Advanced river flood monitoring, modelling and forecasting. J. Comput. Sci. 2014, 4. http://dx.doi.org/10.1016/j.jocs.2014.10.00.
    • (2014) J. Comput. Sci. , pp. 4
    • Merkuryeva, G.1
  • 2
    • 84905982105 scopus 로고    scopus 로고
    • An evolutionary algorithm approach to link prediction in dynamic social networks
    • Bliss C.A., Frank M.R., Danforth C.M., Dodds P.S. An evolutionary algorithm approach to link prediction in dynamic social networks. J. Comput. Sci. 2014, 5:750-764.
    • (2014) J. Comput. Sci. , vol.5 , pp. 750-764
    • Bliss, C.A.1    Frank, M.R.2    Danforth, C.M.3    Dodds, P.S.4
  • 3
    • 84865384544 scopus 로고    scopus 로고
    • Dynamic data-driven genetic algorithm for forest fire spread prediction
    • Denham M., Wendt K., Bianchini G., Cortés A., Margale T. Dynamic data-driven genetic algorithm for forest fire spread prediction. J. Comput. Sci. 2012, 3:398-404.
    • (2012) J. Comput. Sci. , vol.3 , pp. 398-404
    • Denham, M.1    Wendt, K.2    Bianchini, G.3    Cortés, A.4    Margale, T.5
  • 4
    • 78649503580 scopus 로고    scopus 로고
    • Wildland fire growth prediction method based on multiple overlapping solution
    • Bianchini G., Denham M., Cortés A., Margalef T., Luque E. Wildland fire growth prediction method based on multiple overlapping solution. J. Comput. Sci. 2010, 1:229-237.
    • (2010) J. Comput. Sci. , vol.1 , pp. 229-237
    • Bianchini, G.1    Denham, M.2    Cortés, A.3    Margalef, T.4    Luque, E.5
  • 5
    • 84897657079 scopus 로고    scopus 로고
    • Enhanced artificial bee colony for training least squares supportvector machines in commodity price forecasting
    • Mustaffa Z., Yusof Y., Kamaruddin S.S. Enhanced artificial bee colony for training least squares supportvector machines in commodity price forecasting. J. Comput. Sci. 2014, 5:196-205.
    • (2014) J. Comput. Sci. , vol.5 , pp. 196-205
    • Mustaffa, Z.1    Yusof, Y.2    Kamaruddin, S.S.3
  • 6
    • 79953102821 scopus 로고    scopus 로고
    • Twitter mood predicts the stock market
    • Bollen J., Mao H., Zeng X. Twitter mood predicts the stock market. J. Comput. Sci. 2011, 2:1-8.
    • (2011) J. Comput. Sci. , vol.2 , pp. 1-8
    • Bollen, J.1    Mao, H.2    Zeng, X.3
  • 7
    • 84940757656 scopus 로고    scopus 로고
    • Using least square support vector regression with genetic algorithm to forecast beta systematic risk
    • Yuan F.-C., Lee C.-H. Using least square support vector regression with genetic algorithm to forecast beta systematic risk. J. Comput. Sci. 2015, 11:26-33.
    • (2015) J. Comput. Sci. , vol.11 , pp. 26-33
    • Yuan, F.-C.1    Lee, C.-H.2
  • 8
    • 84922646481 scopus 로고    scopus 로고
    • A combination method for interval forecasting of agricultural commodity futures prices
    • Xiong T., Li C., Bao Y., Hu Z., Zhang L. A combination method for interval forecasting of agricultural commodity futures prices. Knowledge-Based Syst. 2015, 77:92-102.
    • (2015) Knowledge-Based Syst. , vol.77 , pp. 92-102
    • Xiong, T.1    Li, C.2    Bao, Y.3    Hu, Z.4    Zhang, L.5
  • 9
    • 84926191810 scopus 로고    scopus 로고
    • A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression
    • Cai Q., Zhang D., Zheng W., Leung S.C.H. A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowledge-Based Syst. 2015, 74:61-68.
    • (2015) Knowledge-Based Syst. , vol.74 , pp. 61-68
    • Cai, Q.1    Zhang, D.2    Zheng, W.3    Leung, S.C.H.4
  • 10
    • 85027923099 scopus 로고    scopus 로고
    • An evolutionary trend reversion model for stock trading rule discovery
    • Zhang X., Hu Y., Xie K., Zhang W., Su L., Liu M. An evolutionary trend reversion model for stock trading rule discovery. Knowledge-Based Syst. 2015, 79:27-35.
    • (2015) Knowledge-Based Syst. , vol.79 , pp. 27-35
    • Zhang, X.1    Hu, Y.2    Xie, K.3    Zhang, W.4    Su, L.5    Liu, M.6
  • 11
    • 84947026669 scopus 로고    scopus 로고
    • Intelligent ensemble forecasting system of stock market fluctuations based on symmetric and asymmetric wavelet functions
    • Lahmiri S., Boukadoum M. Intelligent ensemble forecasting system of stock market fluctuations based on symmetric and asymmetric wavelet functions. Fluctuation Noise Lett. 2015, 10.1142/S0219477515500339.
    • (2015) Fluctuation Noise Lett.
    • Lahmiri, S.1    Boukadoum, M.2
  • 12
    • 84897038591 scopus 로고    scopus 로고
    • Improving forecasting accuracy of the S&P500 intra-day price direction using both wavelet low and high frequency coefficients
    • Lahmiri S. Improving forecasting accuracy of the S&P500 intra-day price direction using both wavelet low and high frequency coefficients. Fluctuation Noise Lett. 2014, 13. 10.1142/S0219477514500084.
    • (2014) Fluctuation Noise Lett. , vol.13
    • Lahmiri, S.1
  • 13
    • 84929168318 scopus 로고    scopus 로고
    • An Ensemble system based on hybrid EGARCH-ANN with different distributional assumptions to predict S&P500 intraday volatility
    • Lahmiri S., Boukadoum M. An Ensemble system based on hybrid EGARCH-ANN with different distributional assumptions to predict S&P500 intraday volatility. Fluctuation Noise Lett. 2015, 14. 10.1142/S0219477515500017.
    • (2015) Fluctuation Noise Lett. , vol.14
    • Lahmiri, S.1    Boukadoum, M.2
  • 15
    • 84920517889 scopus 로고    scopus 로고
    • Biomedical image denoising using variational mode decomposition
    • Lahmiri S., Boukadoum M. Biomedical image denoising using variational mode decomposition. IEEE BIOCAS 2014, 340-343.
    • (2014) IEEE BIOCAS , pp. 340-343
    • Lahmiri, S.1    Boukadoum, M.2
  • 16
    • 84946204242 scopus 로고    scopus 로고
    • Physiological signal denoising with variational mode decomposition and weighted reconstruction after DWT thresholding
    • Lahmiri S., Boukadoum M. Physiological signal denoising with variational mode decomposition and weighted reconstruction after DWT thresholding. IEEE ISCAS 2015, 806-809.
    • (2015) IEEE ISCAS , pp. 806-809
    • Lahmiri, S.1    Boukadoum, M.2
  • 17
    • 84930959694 scopus 로고    scopus 로고
    • Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis
    • Lahmiri S. Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis. Physica A 2015, 437:130-138.
    • (2015) Physica A , vol.437 , pp. 130-138
    • Lahmiri, S.1
  • 19
    • 44649202790 scopus 로고    scopus 로고
    • A new sub-pixel mapping algorithm based on a BP neural network with an observation model
    • Zhang L.P., Wu K., Zhong Y.F., Li P.X. A new sub-pixel mapping algorithm based on a BP neural network with an observation model. Neurocomputing 2008, 71:2046-2054.
    • (2008) Neurocomputing , vol.71 , pp. 2046-2054
    • Zhang, L.P.1    Wu, K.2    Zhong, Y.F.3    Li, P.X.4
  • 20
    • 0029535737 scopus 로고
    • Particle swarm optimization
    • Kennedy J., Eberhart R.C. Particle swarm optimization. IEEE ICNN 1995, 1942-1948.
    • (1995) IEEE ICNN , pp. 1942-1948
    • Kennedy, J.1    Eberhart, R.C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.