Double quantization forecasting method for filling missing data in the CATS time series

The double vector quantization forecasting method based on Kohonen self-organizing maps is applied to predict the missing values of the CATS competition data set. As one of the features of the method is the ability to predict vectors instead of scalar values in a single step, the compromise between the size of the vector prediction and the number of repetitions needed to reach the required prediction horizon is studied. The long-term stability of the double vector quantization method makes it possible to obtain reliable values on a rather long-term forecasting horizon.

[1]  Jukka Heikkonen,et al.  Recurrent SOM with local linear models in time series prediction , 1998, ESANN.

[2]  Jörg A. Walter,et al.  Nonlinear prediction with self-organizing maps , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[3]  M. COTTRELL.  SIMULATING INTEREST RATE STRUCTURE EVOLUTION ON A LONG TERM HORIZON A KOHONEN MAP APPLICATION , .

[4]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[5]  Juha. Vesanto.  Publication 1 Using the SOM and Local Models in Time−Series Prediction Juha Vesanto In Proceedings of Workshop on Self−Organizing Maps , 1997 .

[6]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[7]  Michel Verleysen,et al.  Double SOM for Long-term Time Series Prediction , 2003 .

[8]  Gilles Pagès,et al.  Theoretical aspects of the SOM algorithm , 1998, Neurocomputing.

[9]  Michel Verleysen,et al.  Kohonen maps versus vector quantization for data analysis , 1997, ESANN.

[10]  Francesco Camastra,et al.  Neural Short-Term Prediction Based on Dynamics Reconstruction , 1999, Neural Processing Letters.

[11]  Patrick Rousset,et al.  Forecasting of curves using a Kohonen classification , 1998 .

[12]  Michel Verleysen,et al.  On the Use of Self-Organizing Maps to Accelerate Vector Quantization , 2002, Neurocomputing.

[13]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[14]  Teuvo Kohonen,et al.  In: Self-organising Maps , 1995 .

[15]  Michel Verleysen,et al.  Time series forecasting with SOM and local non-linear models-Application to the DAX 30 index prediction , 2003 .

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