Layered Ensemble Architecture for Time Series Forecasting
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Md. Mustafizur Rahman | Xin Yao | Kazuyuki Murase | Md. Monirul Islam | X. Yao | K. Murase | Md. Mustafizur Rahman | M. Islam
[1] Bogdan Gabrys,et al. A Generic Multilevel Architecture for Time Series Prediction , 2011, IEEE Transactions on Knowledge and Data Engineering.
[2] Fred Collopy,et al. How effective are neural networks at forecasting and prediction? A review and evaluation , 1998 .
[3] Shyi-Ming Chen,et al. Temperature prediction using fuzzy time series , 2000, IEEE Trans. Syst. Man Cybern. Part B.
[4] José Neves,et al. Evolving Time Series Forecasting ARMA Models , 2004, J. Heuristics.
[5] Fred L. Collopy,et al. Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .
[6] D. Marquardt. An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .
[7] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.
[8] Aurora Trinidad Ramirez Pozo,et al. The boosting technique using correlation coefficient to improve time series forecasting accuracy , 2007, 2007 IEEE Congress on Evolutionary Computation.
[9] Hubert Cardot,et al. A new boosting algorithm for improved time-series forecasting with recurrent neural networks , 2008, Inf. Fusion.
[10] A. Timmermann. Forecast Combinations , 2005 .
[11] Haimonti Dutta,et al. Measuring Diversity in Regression Ensembles , 2009, IICAI.
[12] J. Scott Armstrong,et al. Extrapolation for Time-Series and Cross-Sectional Data , 2009 .
[13] Thomas H. Naylor,et al. Box-Jenkins Methods: An Alternative to Econometric Models , 1972 .
[14] Cyril Fonlupt,et al. Applying Boosting Techniques to Genetic Programming , 2001, Artificial Evolution.
[15] B. Sick,et al. Forecasting financial time series with support vector machines based on dynamic kernels , 2008, 2008 IEEE Conference on Soft Computing in Industrial Applications.
[16] Paulo Cortez,et al. Real-Time Forecasting by Bio-Inspired Models , 2002 .
[17] Jörg D. Wichard,et al. Forecasting the NN5 time series with hybrid models , 2011 .
[18] D. N. Prabhakar Murthy,et al. Forecasting maximum speed of aeroplanes — A case study in technology forecasting , 1984, IEEE Transactions on Systems, Man, and Cybernetics.
[19] Nathan Intrator,et al. Boosting Regression Estimators , 1999, Neural Computation.
[20] Robert E. Schapire,et al. The strength of weak learnability , 1990, Mach. Learn..
[21] Nikolaos Kourentzes,et al. An evaluation of neural network ensembles and model selection for time series prediction , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[22] Michael Y. Hu,et al. Linear and nonlinear time series forecasting with artificial neural networks , 1998 .
[23] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[24] Qi Wu,et al. A SHORT-TERM FORECASTING MODEL WITH INHIBITING NORMAL DISTRIBUTION NOISE OF SALE SERIES , 2013, Appl. Artif. Intell..
[25] Weizhong Yan,et al. Toward Automatic Time-Series Forecasting Using Neural Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[26] A. Timmermann. Chapter 4 Forecast Combinations , 2006 .
[27] Fabio Roli,et al. Design of effective neural network ensembles for image classification purposes , 2001, Image Vis. Comput..
[28] Noel E. Sharkey,et al. Combining diverse neural nets , 1997, The Knowledge Engineering Review.
[29] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[30] Hong-Sen Yan,et al. Short-term sales forecasting with change-point evaluation and pattern matching algorithms , 2012, Expert Syst. Appl..
[31] Chee Peng Lim,et al. Predicting drug dissolution profiles with an ensemble of boosted neural networks: a time series approach , 2003, IEEE Trans. Neural Networks.
[32] Predicitions Huanhuan Chen,et al. Ensemble Regression Trees for Time Series , .
[33] R. Clemen. Combining forecasts: A review and annotated bibliography , 1989 .
[34] Bo Yang,et al. Flexible neural trees ensemble for stock index modeling , 2007, Neurocomputing.
[35] Victor L. Berardi,et al. Time series forecasting with neural network ensembles: an application for exchange rate prediction , 2001, J. Oper. Res. Soc..
[36] Fred Collopy,et al. Automatic Identification of Time Series Features for Rule-Based Forecasting , 2001 .
[37] David H. Wolpert,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996, Neural Computation.
[38] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[39] Moacir P. Ponti Jr.. Combining Classifiers: From the Creation of Ensembles to the Decision Fusion , 2011, 2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials.
[40] Xin Yao,et al. Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..
[41] Francisco Herrera,et al. Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study , 2010, IEEE Transactions on Evolutionary Computation.
[42] Bogdan Gabrys,et al. Classifier selection for majority voting , 2005, Inf. Fusion.
[43] H. Jaeger,et al. Stepping forward through echoes of the past : forecasting with Echo State Networks , 2007 .
[44] Robert A. Jacobs,et al. Bias/Variance Analyses of Mixtures-of-Experts Architectures , 1997, Neural Computation.
[45] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[46] Fabio Roli,et al. An approach to the automatic design of multiple classifier systems , 2001, Pattern Recognit. Lett..
[47] M. Ogorzalek,et al. Time series prediction with ensemble models , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[48] Xin Yao,et al. Simultaneous training of negatively correlated neural networks in an ensemble , 1999, IEEE Trans. Syst. Man Cybern. Part B.
[49] Xin Yao,et al. A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.
[50] John A. Bullinaria,et al. Neural network ensembles for time series forecasting , 2009, GECCO '09.
[51] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[52] Tiffany Hui-Kuang Yu,et al. Ratio-based lengths of intervals to improve fuzzy time series forecasting , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[53] Ludmila I. Kuncheva,et al. Switching between selection and fusion in combining classifiers: an experiment , 2002, IEEE Trans. Syst. Man Cybern. Part B.
[54] ZHUO ZHENG. Boosting and Bagging of Neural Networks with Applications to Financial Time Series , 2006 .
[55] Sherif Hashem,et al. Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.
[56] L. Kilian,et al. In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use? , 2002, SSRN Electronic Journal.
[57] Francisco Herrera,et al. An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes , 2011, Pattern Recognit..
[58] Amir F. Atiya,et al. A new Bayesian formulation for Holt's exponential smoothing , 2009 .
[59] J. Scott Armstrong,et al. Principles of forecasting , 2001 .
[60] Rob J Hyndman,et al. Another look at measures of forecast accuracy , 2006 .
[61] Shuichi Kurogi,et al. Forecasting Using First-Order Difference of Time Series and Bagging of Competitive Associative Nets , 2007, 2007 International Joint Conference on Neural Networks.
[62] Lars Kai Hansen,et al. Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[63] Xin Yao,et al. Bagging and Boosting Negatively Correlated Neural Networks , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[64] Xin Yao,et al. Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.
[65] Lisa Werner,et al. Principles of forecasting: A handbook for researchers and practitioners , 2002 .
[66] Amir F. Atiya,et al. Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition , 2011 .
[67] Sven F. Crone,et al. Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction , 2011 .