The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Optimal control by least squares support vector machines
Neural Networks
ϵ-Descending Support Vector Machines for Financial Time Series Forecasting
Neural Processing Letters
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Support Vector Machine Regression for Volatile Stock Market Prediction
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Support Vector Machine for Regression and Applications to Financial Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Modeling chaotic behavior of stock indices using intelligent paradigms
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
A new model for time-series forecasting using radial basis functions and exogenous data
Neural Computing and Applications
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Research on Hybrid ARIMA and Support Vector Machine Model in Short Term Load Forecasting
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Dynamic support vector machines for non-stationary time series forecasting
Intelligent Data Analysis
Customer churning prediction using support vector machines in online auto insurance service
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Application of support vector machine and similar day method for load forecasting
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Recurrent support vector machines in reliability prediction
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Short-Term load forecasting based on self-organizing map and support vector machine
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Direct and recursive prediction of time series using mutual information selection
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Load forecasting using fixed-size least squares support vector machines
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
An overview of statistical learning theory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
Analysis of switching dynamics with competing support vector machines
IEEE Transactions on Neural Networks
Novel approaches for online playout delay prediction in VoIP applications using time series models
Computers and Electrical Engineering
Application notes: dynamic physical behavior analysis for financial trading decision support
IEEE Computational Intelligence Magazine
A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series
Neural Processing Letters
A hybrid stock selection model using genetic algorithms and support vector regression
Applied Soft Computing
Using hyperheuristics under a GP framework for financial forecasting
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Engineering Applications of Artificial Intelligence
The 4 diabetes support system: a case study in CBR research and development
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Symbolic representation of smart meter data
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Load forecasting using a multivariate meta-learning system
Expert Systems with Applications: An International Journal
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
On the investigation of hyper-heuristics on a financial forecasting problem
Annals of Mathematics and Artificial Intelligence
Advanced Engineering Informatics
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Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting, weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. Accurate and unbiased estimation of the time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms. This paper provides a survey of time series prediction applications using a novel machine learning approach: Support Vector Machines (SVM). The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques such as multi-layer perceptrons. The ultimate goal is to provide the reader with insight into the applications using SVM for time series prediction, to give a brief tutorial on SVMs for time series prediction, to outline some of the advantages and challenges in using SVMs for time series prediction, and to provide a source for the reader to locate books, technical journals, and other online SVM research resources.