Combining motif information and neural network for time series prediction

Abstract

Recent research works pay more attention to time series prediction, in which some time series data mining approaches have been exploited. In this paper, we propose a new method for time series prediction which is based on the concept of time series motifs. Time series motif is a previously unknown pattern appearing frequently in a time series. In the proposed approach, we first search for time series motif by using EP-C algorithm and then exploit motif information for forecasting in combination of a neural network model. Experimental results demonstrate our proposed method performs better than artificial neural network ANN in terms of prediction accuracy and time efficiency. Besides, our proposed method is more robust to noise than ANN.