Recurrent SOM with Local Linear Models in Time Series Prediction
Timo Koskela, Markus Varsta, Jukka Heikkonen, Kimmo K. Kaski · 1998
Recurrent Self-Organizing Map (RSOM) is studied in three different time series prediction cases. RSOM is used to cluster the series into local data sets, for which corresponding local linear models are estimated. RSOM includes recurrent difference vector in each unit which allows storing context from the past input vectors. Multilayer perceptron (MLP) network and autoregressive (AR) model are used to compare the prediction results. In studied cases RSOM shows promising results.