12/05/2013

Speaker(s) : Rosangela Ballini (UNICAMP)

Granular models based on fuzzy clustering are presented as an approach for time series forecasting. These models are constructed in two phases. The first one uses the clustering algorithms to find group structures in a historical database. Two different approaches are discussed: fuzzy c-means clustering and participatory learning algorithms. Fuzzy c-mean clustering, which is a supervised clustering algorithm, is used to explore similar data characteristics, such as trend or cyclical components. Participatory learning induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative to construct adaptive fuzzy systems. In the second phase, two cases are considered. In the first case, a regression model is adjusted for each cluster and forecasts are produced by a weighted combination of the local regression models. In the second case, prediction data are classified according to the group structure found in the database. Then, forecasts are produced using the cluster centers weighted by the degree with which prediction data match the groups. The weighted combination of local models constitutes a forecasting approach called granular functional forecasting modeling, and the approach based on weighted combination cluster centers comprises granular relational forecasting modeling. The effectiveness of the granular forecasting approaches is verified using three different applications: average streamflow forecasting, pricing option estimation and modeling of regime changes in Brazilian nominal interest rates.

benjamin.piwowarski (at) nulllip6.fr