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This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The training data patterns are processed incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is added to the most similar cluster. During the clustering...
We present a chaos forecasting system for chaotic time series. After reconstructing the phase space of a chaotic time series, we partition the phase space into some clusters using the fuzzy c-means clustering algorithm. We learn the cluster to which future values will most likely belong. This allows us to make short-term forecasting of the future behavior of a time series by back-propagation network...
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