Hybridising neural network and pattern matching under dynamic time warping for time series prediction

Abstract

Pattern matching-based forecasting models are attractive due to their simplicity and the ability to predict complex nonlinear behaviours. Euclidean measure is the most commonly used metric for pattern matching in time series. However, its weakness is that it is sensitive to distortion in time axis; so, this can influence on forecasting results. The dynamic time warping (DTW) measure is introduced as a solution to the weakness of Euclidean distance metric. In addition, artificial neural networks (ANNs) have been widely used in the time series forecasting. They have been used to capture the complex relationships with a variety of patterns. In this work, we propose an improved hybrid method which is an affine combination of neural network model and DTW-based pattern matching model for time series prediction. This 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 DTW-based pattern matching method used separately in time series prediction.