System identification: theory for the user
System identification: theory for the user
The nature of statistical learning theory
The nature of statistical learning theory
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
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This paper proposes the Spectral Clustering Kernel Machine (SCKM) for times series prediction. Support Vector Machine (SVM), Relevance Vector Machine (RVM) and the Spectral Clustering Kernel Machine (SCKM) are compared in terms of performance accuracy for a simple time series approximation problem. The three outlined algorithms each of which with interesting features to perform automated learning are examined, analysed and empirically tested. In case of the SVM, our tests combine also a preprocessing stage including Kohonen Maps (SOM) as well as K-means clustering. In the case of RVM we also implemented a constructive approach based on the fast marginal likelihood maximization described in [14]. Prediction results in two benchmark time series have been addressed using various performance metrics. The results demonstrate that whereas RVM models achieve larger parsimony of the fitted model, both SVM and SCKM attain higher accuracy. The learning models are competitive for real world problems.