Computational Intelligence Methods for Financial Time Series Modeling

In this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Combining this input space partitioning methodology with feedforward neural networks acting as local predictors for each identified cluster helps alleviate the problem of nonstationarity frequently encountered in real-life applications. An improvement in the one-step-ahead forecasting accuracy was achieved compared to a global feedforward neural network model for the time series of the exchange rate of the German Mark to the US Dollar.

[1]  Michael N. Vrahatis,et al.  The New k-Windows Algorithm for Improving the k-Means Clustering Algorithm , 2002, J. Complex..

[2]  Eamonn J. Keogh,et al.  UCR Time Series Data Mining Archive , 1983 .

[3]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[4]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[5]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[6]  Dimitris K. Tasoulis,et al.  Time Series Forecasting Methodology for Multiple-Step-Ahead Prediction , 2005, Computational Intelligence.

[7]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[8]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[9]  Steven Walczak,et al.  An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks , 2001, J. Manag. Inf. Syst..

[10]  Irwin W. Sandberg,et al.  Uniform Approximation and Gamma Networks , 1997, Neural Networks.

[11]  Irwin W. Sandberg,et al.  Uniform approximation of multidimensional myopic maps , 1997 .

[12]  Dimitris K. Tasoulis,et al.  Financial forecasting through unsupervised clustering and evolutionary trained neural networks , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[13]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[14]  J. Príncipe,et al.  Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control , 1998, Proc. IEEE.

[15]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[16]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

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

[18]  Dimitris K. Tasoulis,et al.  Parallelizing the Unsupervised k-Windows Clustering Algorithm , 2003, PPAM.

[19]  Lijuan Cao,et al.  Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[20]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[21]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[22]  Dimitris K. Tasoulis,et al.  Unsupervised distributed clustering , 2004, Parallel and Distributed Computing and Networks.

[23]  Dimitris K. Tasoulis,et al.  Improving the orthogonal range search k-windows algorithm , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[24]  B Fritzke,et al.  A growing neural gas network learns topologies. G. Tesauro, DS Touretzky, and TK Leen, editors , 1995, NIPS 1995.

[25]  Jingtao Yao,et al.  A case study on using neural networks to perform technical forecasting of forex , 2000, Neurocomputing.

[26]  Richard S. Sutton,et al.  Online Learning with Random Representations , 1993, ICML.

[27]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[28]  Andrew M. Fraser,et al.  Information and entropy in strange attractors , 1989, IEEE Trans. Inf. Theory.

[29]  C. Siriopoulos,et al.  Time series forecasting with a hybrid clustering scheme and pattern recognition , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[30]  Ah Chung Tsoi,et al.  Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference , 2001, Machine Learning.

[31]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[32]  Halbert White,et al.  Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[33]  M. N. Vrahatis,et al.  Adaptive stepsize algorithms for on-line training of neural networks , 2001 .

[34]  Vassilis P. Plagianakos,et al.  Parallel evolutionary training algorithms for “hardware-friendly” neural networks , 2002, Natural Computing.

[35]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[36]  Marie Cottrell,et al.  Some known facts about financial data , 2001, ESANN.

[37]  Ashok N. Srivastava,et al.  Nonlinear gated experts for time series: discovering regimes and avoiding overfitting , 1995, Int. J. Neural Syst..

[38]  R. W. Dobbins,et al.  Computational intelligence PC tools , 1996 .

[39]  Allan Pinkus,et al.  Approximation theory of the MLP model in neural networks , 1999, Acta Numerica.

[40]  Ruy Luiz Milidiú,et al.  Time-series forecasting through wavelets transformation and a mixture of expert models , 1999, Neurocomputing.

[41]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[42]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.