Conference Paper
A Novel Time Series Forecasting Approach with MultiLevel Data Decomposing and Modeling
Coll. of Comput. Sci., Zhejiang Univ., Hangzhou
DOI: 10.1109/WCICA.2006.1712645 Conference: Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, Volume: 1 Source: IEEE Xplore
 Citations (13)
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ABSTRACT: This paper evaluates different procedures for selecting the order of a nonseasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized BoxJenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. Our results show that many objective selection criteria provide structures equal or superior to the timeconsuming BJ method. For the sets of data used in this study, we also examine the influence of parsimony in timeseries forecasting. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.Journal of Forecasting 07/2009; 13(5):419  434. · 0.93 Impact Factor  Proceedings of the National Academy of Sciences 02/1956; 42(1):437. · 9.81 Impact Factor

Conference Paper: A study of nonperiodic shortterm random walk forecasting based on RBFNN, ARMA, or SVRGM(1,1τ) approach
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ABSTRACT: This paper introduces several prediction models for shortterm random walk forecasting like stock price indexes forecasting. The radial basis function neural net (RBFNN) is widely applied to function approximation or classification issues. An autoregressive movingaverage method has been utilized on the topic of time series. SVRGM(1,1τ) model employ the support vector machines (SVM) learning algorithm to improve the control and environment parameters in GM(1,1τ) model, that is, enhancing generalization capability in the nonperiodic shortterm prediction. Therefore, this proposed method could smooth the overshooting problem, that often occurred in GM(1,1τ) model or autoregressive movingaverage (ARMA) method, so as to achieve better prediction accuracy. Finally, the comparison of performance on international stock price indexes forecasting between the various models has been done.Neural Networks, 2003. Proceedings of the International Joint Conference on; 08/2003
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