Time Series Prediction using Recurrent SOM with Local Linear Models
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چکیده
A newly proposed Recurrent Self-Organizing Map (RSOM) is studied in time series prediction. In this approach RSOM is used to cluster the data to local data sets and local linear models corresponding each of the map units are then estimated based on the local data sets. A traditional way of clustering the data is to use a windowing technique to split it to input vectors of certain length. In this procedure, the temporal context between the consecutive vectors is lost. In RSOM the map units keep track of the past input vectors with a recurrent di erence vector in each unit. The recurrent structure allows the map to store information concerning the change in the magnitude and direction of the input vector. RSOM can thus be used to cluster the temporal context in the time series. This allows a di erent local model to be selected based on the context and the current input vector of the model. The studied cases show promising results.
منابع مشابه
Recurrent SOM with local linear models in time series prediction
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تاریخ انتشار 1997