Forecasting of clustered time series with recurrent neural networks and a fuzzy clustering scheme

Authors:
Hans Georg Seedig;Ralph Grothmann;Thomas A. Runkler
Affiliations:
Mathematics and Computer Science at Technische Universität München and University of Hagen, Germany;Siemens Corporate Technology, Munich, Germany;Learning Systems Department, Siemens Corporate Technology, Munich, Germany
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Fuzzy c-neural network models (FCNNM) combine clustering techniques with advanced neural networks for time series modeling in order to make predictions for a possibly large set of time series using only a small number of models. Given a set of time series, FCNNM finds a partition matrix that quantifies to which degree each time series is associated with each prediction model, as well as the parameters of the neural network models for each cluster. FCNNM allows to automatically identify groups of time series with similar dynamics. This results in higher data efficiency, being of particular interest in cases of poor data availability. We illustrate the application of FCNNM to cash withdrawal series as part of an effective cash management.