Ensemble Incremental Random Vector Functional Link Network for Short-term Crude Oil Price Forecasting
暂无分享,去创建一个
Ponnuthurai N. Suganthan | Xueheng Qiu | Gehan A. J. Amaratunga | G. Amaratunga | P. Suganthan | Xueheng Qiu
[1] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[2] Patrick Flandrin,et al. A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[3] K. Lai,et al. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm , 2008 .
[4] Xin Geng,et al. Incremental Learning , 2009, Encyclopedia of Biometrics.
[5] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[6] Kin Keung Lai,et al. Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price , 2016 .
[7] Ponnuthurai N. Suganthan,et al. Detecting Wind Power Ramp with Random Vector Functional Link (RVFL) Network , 2015, 2015 IEEE Symposium Series on Computational Intelligence.
[8] Jianping Li,et al. A deep learning ensemble approach for crude oil price forecasting , 2017 .
[9] Dejan J. Sobajic,et al. Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.
[10] Yuting Wang,et al. Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering , 2013, IEEE Transactions on Power Systems.
[11] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[12] 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.
[13] 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).
[14] R. E. Lee,et al. Distribution-free multiple comparisons between successive treatments , 1995 .
[15] 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).
[16] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[17] P. N. Suganthan,et al. A comprehensive evaluation of random vector functional link networks , 2016, Inf. Sci..
[18] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[19] Ponnuthurai N. Suganthan,et al. Random vector functional link network for short-term electricity load demand forecasting , 2016, Inf. Sci..
[20] Long Chen,et al. Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation , 2017 .
[21] Jürgen Schmidhuber,et al. Recurrent nets that time and count , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[22] Dejan J. Sobajic,et al. Neural-net computing and the intelligent control of systems , 1992 .
[23] J. Elman. Learning and development in neural networks: the importance of starting small , 1993, Cognition.
[24] 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.
[25] 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.
[26] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[27] Ponnuthurai N. Suganthan,et al. Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] , 2016, IEEE Computational Intelligence Magazine.
[28] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[29] Rahmat-Allah Hooshmand,et al. A hybrid intelligent algorithm based short-term load forecasting approach , 2013 .
[30] Yue Zhang,et al. Deep Learning for Event-Driven Stock Prediction , 2015, IJCAI.
[31] M. Friedman. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .