Research On Local Model-Based Time Series Prediction

Time series analysis has caught the focus of many researchers, and becomes a hot research field with great theoritical value and application value. Time series forcasting is the main task of time series analysis, and has been widely applied in many fields such as industry automatic, Hydrological, Geology, stock market, military science and so on.Nowadays, global model is the main tool for time series predicting, but it suffers low prediction efficiency, low prediction accuracy and high computation complexity for model updating. In recent years, the techniques such as data mining, pattern recognition, signal processing and chaos so on are incorporated into time series prediction, which divides the time series data in time domain or frequency domain and constructs the prediction models in local time domain or frequency domain. Local model for time series prediction can obtain more accuracy prediction results, and has lower complexity of models and lower computation complexity of modeling. But there are still many problems worth doing some research. In this paper, local modeling for time series prediction is discussed in the decomposition field and time field respectively, and mainly research focuses on the boundary effect processing method for empirical mode decomposition, model selection and updating in decomposition field, adaptive time series data clustering with arbitrary shape, nonlinear feature extraction and fast attribute reduction of time series classification, time series prediction modeling in local time field with support vector and so forth. The main contributions of this dissertation are as follows:Firstly, similarity searching based boundary effect processing method for empirical mode decomposition is proposed. This method utilise the property of local self-similarity of a nonlinear or linear signal to extend series and get extra extrema for spline interpolation, make extended series more similar to the real series before the fore-endpoint or behind of the back-endpoint and reduces the boundary effect greatly. Furthermore, the computing complexity is low for using fast nearest neighbor searching.Experimental results validated the effectiveness of this method. Secondly, RBF network and Incremental Independent Vector Combination Predicting algorithm in Kernel Space are proposed to construct the forcasting model for every intrinsic mode function component of time series decomposed with empirical mode decomposition method. But empirical mode decomposition aggravates computation for parameter selection of forcasting models. In order to resolve this problem, model parameter selection is done only for two IMFs, and other model parameters are computed by the relationship between the model parameters and the IMFs, and computation burden for model selection is alleaviated. Furthermore, for low updating efficiency of RBF network, Incremental Independent Vector Combination Predicting algorithm in Kernel Space is proposed and has lower computation complexity. Experimental results show that time series prediction in the empirical mode decomposition domain with RBF neural network and IIVCPKS algorithm can obtain more accuracy prediction than single model.Thirdly, a novel validity index for clustering with penalizing method is proposed to estimate the cluster number. A penal factor is introduced into this validity index and makes the curve of this validity index convex-like or almost convex-like. A more accuracy estimated cluster number can be obtained by minimize this validity index. Experimental results show that the proposed validity index can estimate correct or almost correct cluster number.Finally, efficient rough-set-based attribute reduction algorithm with function maping is proposed. In this algorithm, a fast nearest neighbour searching method with gradually shrinking search space is proposed to reduce the computing complexity of indiscernibility relation, positive relation and so on. Experimental results show that the proposed algorithm computed attribute reduction more efficiently and had good scalability with data size and data dimension.