Hybridizing pattern matching and neural network for time series prediction

Abstract

Time series prediction is a non-trivial task for forecasters in various areas. In this paper, we introduce a new hybrid method for time series prediction. This hybrid method is a combination of k-nearest neighbors method and neural network model. The new method can take full advantage of the individual strengths of the two models to create a more effective approach for time series prediction. Experimental results show that our proposed method outperforms neural network model and k-nearest neighbors method used separately in time series prediction.