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Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

Computers, Materials & Continua 2023年第2期74卷 3199-3219页

作者： Weifeng Liu Xin Yu Qinyang Zhao Guang Cheng Xiaobing Hou Shengqi He college of urban rall translt and logistics Beijing Union UniversityBeijing100101China Beijing Key Laboratory of Information Service Engineering Beijing Union UniversityBeijing100101China Faculty of Business Economicsand LawThe University of QueenslandBrisbane4072Australia Department of Oncology Wangjing HospitalChina Academy of Chinese Medical SciencesBeijing100102China

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

关键词： Time series data prediction regression analysis long short-term memory network prophet

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