Vector quantization: a weighted version for time-series forecasting

Nonlinear time-series prediction offers potential performance increases compared to linear models. Nevertheless, the enhanced complexity and computation time often prohibits an efficient use of nonlinear tools. In this paper, we present a simple nonlinear procedure for time-series forecasting, based on the use of vector quantization techniques; the values to predict are considered as missing data, and the vector quantization methods are shown to be compatible with such missing data. This method offers an alternative to more complex prediction tools, while maintaining reasonable complexity and computation time.

[1]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[2]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[3]  Michel Verleysen,et al.  Learning in RBF networks , 1996 .

[4]  Amaury Lendasse,et al.  Time series forecasting with SOM and local non-linear models - Application to the DAX30 index prediction , 2003 .

[5]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[6]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[7]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

[8]  J. William Ahwood,et al.  CLASSIFICATION , 1931, Foundations of Familiar Language.

[9]  Michel Verleysen,et al.  Width optimization of the Gaussian kernels in Radial Basis Function Networks , 2002, ESANN.

[10]  Smaïl Ibbou.  Classification, analyse des correspondances et methodes neuronales , 1998 .

[11]  Michel Verleysen,et al.  Approximation by Radial-Basis Function networks - Application to option pricing , 2003 .

[12]  M. Birattari,et al.  Lazy learning for local modelling and control design , 1999 .

[13]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[14]  J. Mason,et al.  Algorithms for approximation , 1987 .

[15]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[16]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .