The nature of statistical learning theory
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Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Accurate on-line support vector regression
Neural Computation
Modeling complex environmental data
IEEE Transactions on Neural Networks
Implementation Issues of an Incremental and Decremental SVM
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Analysis and Prediction of Air Quality Data with the Gamma Classifier
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Expert Systems with Applications: An International Journal
Vector projection method for unclassifiable region of support vector machine
Expert Systems with Applications: An International Journal
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Artificial Intelligence Review
Immediate water quality assessment in shrimp culture using fuzzy inference systems
Expert Systems with Applications: An International Journal
Granular support vector machine based on mixed measure
Neurocomputing
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For time-series forecasting problems, there have been several prediction models to data, but the development of a more accurate model is very difficult because of high non-linear and non-stable relations between input and output data. Almost all the models at hand are not applicable online, although online prediction, especially for air quality parameters forecasting, has very important significance for real-world applications. A support vector machine (SVM), as a novel and powerful machine learning tool, can be used for time-series prediction and has been reported to perform well by some promising results. This paper develops an online SVM model to predict air pollutant levels in an advancing time-series based on the monitored air pollutant database in Hong Kong downtown area. The experimental comparison between the online SVM model and the conventional SVM model (non-online SVM model) demonstrates the effectiveness and efficiency in predicting air quality parameters with different time series.