Time Series Prediction with the Self-Organizing Map: A Review2018 Apr 04
Summary. We provide a comprehensive and updated survey on applications of
Kohonen’s self-organizing map (SOM) to time series prediction (TSP). The main
goal of the paper is to show that, despite being originally designed as an unsupervised
learning algorithm, the SOM is flexible enough to give rise to a number of
efficient supervised neural architectures devoted to TSP tasks. For each SOM-based
architecture to be presented, we report its algorithm implementation in detail. Similarities and differences of such SOM-based TSP models with respect to standard
linear and nonlinear TSP techniques are also highlighted. We conclude the paper
with indications of possible directions for further research on this field.
Conclusion: In this paper we reviewed several applications of Kohonen’s SOM-based models to time series prediction. Our main goal was to show that the SOM can perform efficiently in this task and can compete equally with well-known neural
architectures, such as MLP and RBF networks, which are more commonly
used. In this sense, the main advantages of SOM-based models over MLPor
RBF-based models are the inherent local modeling property, which favors
the interpretability of the results, and the facility in developing growing architectures, which alleviates the burden of specifying an adequate number of neurons (prototype vectors).