Abstract
We are interested in local models for time series forecasting and we propose a new approach based on hierarchical clustering. This new approach uses a binary tree, k-means clustering and pruning strategies to find out the adequate clustering with MLP and SVM as predictors. The experimentations give very good results for this approach. Three strategies were tested and a comparative study with other methods show that the hierarchical predictor model outperformed the existing models on the three used datasets.
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Cherif, A., Cardot, H., Boné, R. (2013). Hierarchical Clustering for Local Time Series Forecasting. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_8
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DOI: https://doi.org/10.1007/978-3-642-42042-9_8
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