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The technology of phase space construction and Support Vector Machines(SVM) is introduced firstly. Then a novel complex time series forecasting approach based on SVM is proposed. The complex time series is decomposed into long-term trend series and short-term fluctuation series. The SVM regressive forecasting model is constructed respectively. The proposed(More)
The theories of phase space reconstruction and Support Vector Machines (SVM) are introduced firstly. A novel time series forecasting model based on wavelet and SVM is proposed. It first performances multi-scaled decomposition on complex time series using discrete wavelet transformation. Then the reconstructed approximate series and detail series are(More)
This Qualitative abstract representation of time series is a precondition of pattern discovery. A novel method for time series symbolization based on singular event features clustering is proposed in this paper. The first step of it is to extract singular event features based on multi-scale wavelet which can divide time series into event sequences with(More)
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