A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting
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[1] Ming Li,et al. Visiting Power Laws in Cyber-Physical Networking Systems , 2012 .
[2] K. Chau,et al. Neural network and genetic programming for modelling coastal algal blooms , 2006 .
[3] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[4] Junwei Wang,et al. Chaos Control of a Fractional-Order Financial System , 2010 .
[5] Raimondo Manca,et al. Modeling and Pricing of Variance and Volatility Swaps for Local Semi-Markov Volatilities in Financial Engineering , 2010 .
[6] Pei-Chann Chang,et al. Database Classification by Integrating a Case-Based Reasoning and Support Vector Machine for Induction , 2010, J. Circuits Syst. Comput..
[7] François de Vieilleville,et al. Analysis and Comparative Evaluation of Discrete Tangent Estimators , 2005, DGCI.
[8] Xiaohua Yang,et al. A new adaptive local linear prediction method and its application in hydrological time series. , 2010 .
[9] B. R. Badrinath,et al. The distinctive design characteristic of a wireless sensor network: the energy map , 2004, Computer Communications.
[10] Ping-Feng Pai,et al. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .
[11] Bodo Vogt,et al. Power law distribution in high frequency financial data? An econometric analysis , 2011 .
[12] James V. Candy,et al. Adaptive and Learning Systems for Signal Processing, Communications, and Control , 2006 .
[13] Ming Li,et al. Viewing Sea Level by a One-Dimensional Random Function with Long Memory , 2011 .
[14] Ravi Sankar,et al. Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.
[15] Pei-Chann Chang,et al. Emotion classification by removal of the overlap from incremental association language features , 2011 .
[16] Pei-Chann Chang,et al. A TSK type fuzzy rule based system for stock price prediction , 2008, Expert Syst. Appl..
[17] Annamaria Bianchi,et al. Financial Applications of Bivariate Markov Processes , 2011 .
[18] Tomaso A. Poggio,et al. Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.
[19] S. Krishnan,et al. Knowledge-Based Green's Kernel for Support Vector Regression , 2010 .
[20] Shlomo Havlin,et al. Long term memory in extreme returns of financial time series , 2009 .
[21] J. Wang,et al. Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market , 2011 .
[22] Ming-Wei Chang,et al. Load forecasting using support vector Machines: a study on EUNITE competition 2001 , 2004, IEEE Transactions on Power Systems.
[23] David D. Jensen,et al. Mining of Concurrent Text and Time Series , 2008 .
[24] F. Diebold,et al. Comparing Predictive Accuracy , 1994, Business Cycles.
[25] Pei-Chann Chang,et al. Generalized nonlinear discriminant analysis and its small sample size problems , 2011, Neurocomputing.
[26] Pei-Chann Chang,et al. Application of a Case Base Reasoning Based Support Vector Machine for Financial Time Series Data Forecasting , 2009, ICIC.
[27] P. Manimaran,et al. Characterizing multi-scale self-similar behavior and non-statistical properties of fluctuations in financial time series , 2010, 1003.2539.
[28] Huaiqing Wang,et al. Pattern Recognition in Stock Data Based on a New Segmentation Algorithm , 2007, KSEM.
[29] Desheng Dash Wu,et al. Power load forecasting using support vector machine and ant colony optimization , 2010, Expert Syst. Appl..
[30] Weifeng Liu,et al. Adaptive and Learning Systems for Signal Processing, Communication, and Control , 2010 .
[31] Eamonn J. Keogh,et al. An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback , 1998, KDD.
[32] Ming Li. Fractal Time Series—A Tutorial Review , 2010 .
[33] Xian-ping Ge,et al. Pattern Matching in Financial Time Series Data , 1998 .
[34] Ping-Feng Pai,et al. A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .
[35] De Wu,et al. A Piecewise Linear Representation Method of Time Series Based on Feature Points , 2007, KES.
[36] Zonghua Liu. Chaotic Time Series Analysis , 2010 .
[37] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[38] Donghui Zhang,et al. Online event-driven subsequence matching over financial data streams , 2004, SIGMOD '04.
[39] Xiaohua Yang,et al. Chaotic Bayesian Method Based on Multiple Criteria Decision making (MCDM) for Forecasting Nonlinear Hydrological Time Series , 2009 .
[40] Cathy W. S. Chen,et al. An empirical evaluation of fat-tailed distributions in modeling financial time series , 2008, Math. Comput. Simul..
[41] Hong Wang,et al. Chaotic time series analysis of vision evoked EEG , 2010, ICMIT: Mechatronics and Information Technology.
[42] Richard J. Kish,et al. Technical trading strategies and return predictability: NYSE , 2002 .
[43] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[44] Pei-Chann Chang,et al. Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[45] Wei-Chiang Hong,et al. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model , 2009 .