An Enhanced LSTM for Trend Following of Time Series

Yao Hu, Xiaoyan Sun, Xin Nie, Yuzhu Li, Lian Liu
2019 IEEE Access  
Mining and analysis of time series data (TSD) have drawn a great concern, especially in the TSD clustering, classification, and forecast. In the industrial field, e.g., the work condition monitoring and the environmental safety, it is crucial to follow the trend of the corresponding TSD for a safety forecast, and few studies have been devoted to such a trend following. Motivated by this, we propose a trend following the strategy of TSD by using a long short-term memory (LSTM) network for safety
more » ... forecast, in which the training method aggregates the particle swarm optimization (PSO) algorithm with gradient descent (GD) to obtain more competitive model parameters. Three kinds of trend representations of TSD are first defined based on the corresponding research in stock option. Then, the LSTM optimized with the PSO-GD is developed to perform the trend following. From the viewpoint of safety forecast, the trends varied in different time length are further predicted and analyzed. The superiority of the proposed algorithm is experimentally demonstrated by applying it to the electromagnetic radiation intensity TSD sampled from an actual coal mine and PM2.5 in UCI repository. INDEX TERMS LSTM, PSO, safety farecast, series data, trend following. 34020 2169-3536
doi:10.1109/access.2019.2896621 fatcat:ushwwvm6ybhibejuai4brdd2ru