Predict Time Series with Multiple Artificial Neural Networks

Time series prediction is a challenging research area with broad application prospects in machine learning. Accurate prediction on a time series’ value can provide important information for the decision-makers. In the literature, many works were reported to extend different architecture of artificial neural networks to work with time series prediction. However, most of the work only considered the target time series itself, while neglecting the impact of the relevant time series. In this paper we proposed a novel method MANNP that makes use of multiple artificial neural networks to conduct the time series prediction. The proposed method creates time series model and forecast time series. To verify the effectiveness of the proposed method, we apply MANNP to a shipping price index time series prediction. The experimental results show that this method can improve accuracy of prediction when compared with traditional methods.