Abstract
Traditional approach to predict large-scale sequential curves is to build model separately according to every curve, which causes heavy and complicated modeling workload inevitably. A new method is proposed in this paper to solve this problem. By reducing model types of curves, clustering curves and modeling by clusters, the new method simplifies modeling work to a large extent and reserves original information as possible in the meantime. This paper specifies the theory and algorithm, and applies it to predict GDP curves of multi-region, which confirms practicability and validity of the presented approach.
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Long, W., Wang, H. (2008). Predictive Modeling of Large-Scale Sequential Curves Based on Clustering. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2008. ICCS 2008. Lecture Notes in Computer Science, vol 5102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69387-1_55
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DOI: https://doi.org/10.1007/978-3-540-69387-1_55
Publisher Name: Springer, Berlin, Heidelberg
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