Fusion of multiple indicators with ensemble incremental learning techniques for stock price forecasting
暂无分享,去创建一个
Ponnuthurai Nagaratnam Suganthan | Xueheng Qiu | Gehan A. J. Amaratunga | G. Amaratunga | P. Suganthan | Xueheng Qiu
[1] Ponnuthurai N. Suganthan,et al. Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] , 2016, IEEE Computational Intelligence Magazine.
[2] Yuting Wang,et al. Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering , 2013, IEEE Transactions on Power Systems.
[3] L. Breiman. Stacked Regressions , 1996, Machine Learning.
[4] Esmaeil Hadavandi,et al. A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price , 2015, Appl. Soft Comput..
[5] Ingoo Han,et al. Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index , 2000 .
[6] Shie-Jue Lee,et al. A multiple-kernel support vector regression approach for stock market price forecasting , 2011, Expert Syst. Appl..
[7] Sergio Ortobelli Lozza,et al. Fusion of multiple diverse predictors in stock market , 2017, Inf. Fusion.
[8] Ömer Kaan Baykan,et al. Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..
[9] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[10] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[11] Ponnuthurai N. Suganthan,et al. Random vector functional link network for short-term electricity load demand forecasting , 2016, Inf. Sci..
[12] 田中 勝人. D. B. Percival and A. T. Walden: Wavelet Methods for Time Series Analysis, Camb. Ser. Stat. Probab. Math., 4, Cambridge Univ. Press, 2000年,xxvi + 594ページ. , 2009 .
[13] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[14] Norden E. Huang,et al. Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..
[15] Farookh Khadeer Hussain,et al. Support vector regression with chaos-based firefly algorithm for stock market price forecasting , 2013, Appl. Soft Comput..
[16] A. Grossmann,et al. DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE , 1984 .
[17] Donald B. Percival,et al. An introduction to wavelet analysis with applications to vegetation time series , 2004 .
[18] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[19] Vadlamani Ravi,et al. Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network , 2017, Appl. Soft Comput..
[20] P. N. Suganthan,et al. A comprehensive evaluation of random vector functional link networks , 2016, Inf. Sci..
[21] Kin Keung Lai,et al. A neural-network-based nonlinear metamodeling approach to financial time series forecasting , 2009, Appl. Soft Comput..
[22] C. Holt. Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .
[23] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[24] R. Engle. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .
[25] Dejan J. Sobajic,et al. Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.
[26] Maria Q. Feng,et al. A technique to improve the empirical mode decomposition in the Hilbert-Huang transform , 2003 .
[27] K. Satheesh Kumar,et al. Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition , 2016 .
[28] N. Huang,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[29] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[30] J. Nazuno. Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .
[31] Ronald L. Rivest,et al. Introduction to Algorithms , 1990 .
[32] Ponnuthurai Nagaratnam Suganthan,et al. Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting , 2017, Appl. Soft Comput..
[33] Ponnuthurai Nagaratnam Suganthan,et al. Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[34] Georges A. Darbellay,et al. Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .
[35] Robert P. W. Duin,et al. Feedforward neural networks with random weights , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.
[36] T. Bollerslev,et al. Generalized autoregressive conditional heteroskedasticity , 1986 .
[37] Kin Keung Lai,et al. Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price , 2016 .
[38] Ponnuthurai N. Suganthan,et al. Detecting Wind Power Ramp with Random Vector Functional Link (RVFL) Network , 2015, 2015 IEEE Symposium Series on Computational Intelligence.
[39] Dejan J. Sobajic,et al. Neural-net computing and the intelligent control of systems , 1992 .
[40] Sahil Shah,et al. Predicting stock market index using fusion of machine learning techniques , 2015, Expert Syst. Appl..
[41] C. L. Philip Chen,et al. A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction , 1999, IEEE Trans. Syst. Man Cybern. Part B.
[42] Takashi Matsubara,et al. Deep learning for stock prediction using numerical and textual information , 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).
[43] Rahmat-Allah Hooshmand,et al. A hybrid intelligent algorithm based short-term load forecasting approach , 2013 .
[44] Chao Chen,et al. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks , 2012 .
[45] Le Zhang,et al. Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).
[46] Yue Zhang,et al. Deep Learning for Event-Driven Stock Prediction , 2015, IJCAI.
[47] T. Hesterberg,et al. A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.
[48] M. Friedman. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .
[49] Kalyanmoy Deb,et al. Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms , 2017, Swarm Evol. Comput..