Self-organization as an iterative kernel smoothing process
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Self-Organizing Maps
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
ANNSTLF-a neural-network-based electric load forecasting system
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
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The interest in electricity load forecasting has grown up in the last years. However, the accurate load prediction remains a difficult task due particularly to the non-linear character of the time series and the periodical and seasonal patterns it exhibits. Several machine learning techniques such as the Support Vector Machines (SVM) have been developed that are able to deal with non-linear time series. However, the patterns of electricity demand change strongly and periodically with seasons, holidays and other factors. Therefore global models such as the SVM are not expected to perform well. In this paper we propose a new segmentation algorithm based on the Self Organizing Maps (SOM) to split the time series into homogeneous regions. Next, a linear SVM is locally trained in each region. The algorithm proposed has been applied to the prediction of the maximum daily electricity demand. The experimental results show that the new segmentation algorithm helps to improve several well known forecasting techniques.