Time Series Segmentation And Prediction Based On Intelligent Computing Technology

Hydrometeorological time series such as streamflow, precipitation and temperature are among the basic data used to study earth-related phenomena. Evolutionary and disruptive changes in the environment caused by natural factors and human activities result in trends or jumps in hydrometeorological time series. Segmentation and prediction for hydrometeorological time series help men understand natural factors or human-induced changes in human living environment. Due to the importance in research and practice, segmentation and prediction of hydrometeorological records such as streamflow, precipitation and temperature have been received extensive attention. Segmentation and prediction for hydrometeorological time series have always been important topics in hydrometeorological sciences. The emerging intelligent computing techniques have shown their power in computational efficiency, and have been widely applied in segmentation and prediction of hydrometeorological time series. In this paper, intelligent computing techniques are conducted to segmentation and prediction of hydrometeorological time series. The main research contents of this paper include three parts:Firstly, an improved Gath-Geva clustering algorithm is proposed for automatic fuzzy segmentation of univariate and multivariate hydrometeorological time series. The algorithm considers time series segmentation problem as Gath-Geva clustering with the minimum message length criterion as segmentation order selection criterion. One characteristic of the improved Gath-Geva clustering algorithm is its unsupervised nature which can automatically determine the optimal segmentation order. Another characteristic is the application of the modified component-wise expectation maximization algorithm in Gath-Geva clustering which can avoid the drawbacks of the classical expectation maximization algorithm:the sensitivity to initialization and the need to avoid the boundary of the parameter space. The other characteristic is the improvement of numerical stability by integrating segmentation order selection into model parameter estimation procedure.Secondly, a competitive fuzzy clustering algorithm is presented for automatic fuzzy segmentation of multivariate time series. The proposed algorithm is capable of automatically choosing the clustering number and selecting the "split" or "merge" operations efficiently based on the new competitive mechanism. It is insensitive to the initial configuration of the cluster component number and model parameters. Experiments on synthetic data show that the proposed algorithm is able to handle time-varying characteristics of multivariate time series:changes in the mean:changes in the variance; and changes in the correlation structure among the variables. Thirdly, noticed that the structures of the commonly implemented neural networks for the prediction of hydrometeorological time series are static, while the static neural network cannot represent the changes of system dynamics efficiently. In this paper, the application of a sequential learning radial basis function is presented for accurate real-time prediction of hydrometeorological time series. The prediction model employs a sliding data window as dynamical observer, and tunes the structure and parameters of radial basis function neural network to adapt to the dynamical changes of hydrometeorological time series.The three algorithms have been experimentally tested on artificial and hydrometeorological time series. The obtained results show the effectiveness of the algorithm for time series segmentation and prediction.