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Based on prediction model of fuzzy classification and fuzzy recognition combined with statistical correlation, the theory of wavelet analysis was introduced and proposed the prediction model of wavelet and fuzzy combined with statistical correlation, there is a basic mentality: first, wavelet decomposes the prediction factors one by one, and then take the wavelet series that have decomposed into the...
A predictive model of water-quality, which based on wavelet transform and support vector machine, is proposed. This model uses wavelet transform to get water time sequence variations in different scale, and optimizes three parameters of Regression Support Vector Machine with improved Particle Swarm Optimization algorithm, to improve the accuracy of prediction model. This model is used to take one-step...
Traffic flow is a fundamental measure in transportation. Accurate traffic flow prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. A novel multiscale wavelet support vector regression method (MW-SVR) is proposed for traffic flow prediction. Based on wavelet multi-resolution analysis, a scaling kernel function with multi-resolution...
The aim of this paper is to find a model to forecast 1-month ahead monthly sardines catches using a multivariate polynomial model combined with multi-scale stationary wavelet decomposition. The observed monthly sardines catches are decomposed into various sub-series employing wavelet decomposition and then appropriate sub-series are used as inputs to the autoregressive forecasting model. The forecasting...
This paper proposes a novel multi scale nonlinear ensemble methodology for analyzing and modeling the complex exchange rate behaviors. Using several techniques integrated under the proposed unified framework, it deals with data characteristics such as autocorrelation, multi scale heterogeneity and parameter instability during the modeling process. The multi scale heterogeneity property is modeled...
This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is utilized as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model...
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