A New Approach for Short-term Time Series Forecasting. (October 2019)

• Record Type: Journal Article
• Authors: Fan, Henghai; Wu, Shuai; Chen, Ning; Gao, Bo; Xu, Yiming
• Is Part Of: IOP conference series. Volume 646(2019)
• Publication Date: 2019-10
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/646/1/012015
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Short-term series forecasting is one of the essential issues in a variety of tasks, such as traffic flow prediction, stock market tendency analysis, etc. Most current methods based on stable or abundant historical data. In this paper, we proposed a novel model called LA-NN. It takes advantages of both long short-term memory (LSTM) network and autoregressive integrated moving average (ARIMA) by a relation integration of them. So as to deal with the situation of insufficient historical data and sudden abnormal changes in data. A comparison with other representative forecast models validates that the proposed LA-NN network can achieve a better performance.