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

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.

[1]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Ralph Grothmann,et al.  Multi agent market modeling based on neutral networks , 2002 .

[3]  J. Navarro-Pedreño.  Numerical Methods for Least Squares Problems , 1996 .

[4]  Marc Wildi.  Signal Extraction: Efficient Estimation, 'Unit Root'-Tests and Early Detection of Turning Points , 2004 .

[5]  John F. Kolen,et al.  Neural Network Architectures for the Modeling of Dynamic Systems , 2001 .

[6]  J. Bezdek,et al.  VAT: a tool for visual assessment of (cluster) tendency , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[7]  Amir F. Atiya,et al.  A new Bayesian formulation for Holt's exponential smoothing , 2009 .

[8]  Sven F. Crone,et al.  Forecasting with Computational Intelligence - An Evaluation of Support Vector Regression and Artificial Neural Networks for Time Series Prediction , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[9]  J. van Leeuwen,et al.  Neural Networks: Tricks of the Trade , 2002, Lecture Notes in Computer Science.

[10]  John F. Kolen,et al.  Field Guide to Dynamical Recurrent Networks , 2001 .

[11]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[12]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[13]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[14]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[15]  R.J. Hathaway,et al.  Switching regression models and fuzzy clustering , 1993, IEEE Trans. Fuzzy Syst..

[16]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[17]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[18]  Abdol S. Soofi,et al.  Modelling and Forecasting Financial Data , 2002 .

[19]  Rudolf Sollacher,et al.  Efficient online learning with Spiral Recurrent Neural Networks , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[20]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[21]  Thomas A. Runkler,et al.  Fuzzy c-auto regression models , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).