Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Self-Organizing Maps
Fast learning in networks of locally-tuned processing units
Neural Computation
Least squares quantization in PCM
IEEE Transactions on Information Theory
Random Ordinality Ensembles$\colon$ A Novel Ensemble Method for Multi-valued Categorical Data
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A co-training approach for time series prediction with missing data
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Deterministic vector long-term forecasting for fuzzy time series
Fuzzy Sets and Systems
Long-term time series prediction using OP-ELM
Neural Networks
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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.