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Application of deep learning model under improved emd in railway transportation investment benefits and national economic attribute analysis
The Journal of Supercomputing ( IF 2.5 ) Pub Date : 2021-01-18 , DOI: 10.1007/s11227-020-03609-z
Jia He

The railway transportation industry is one of the essential factors to promote economic development, so research is made on improving the investment benefit of construction planning of the railway transportation industry, and on this basis, the national emergency attribute is analyzed. A prediction model of railway transportation investment benefits and national economic attributes based on EEMD-LSTM (ensemble empirical mode decomposition—long-short-term memory) model is proposed. The EEMD algorithm is used to decompose the daily investment price of the railway transportation industry to obtain the IMF (intrinsic mode function) with different cycle characteristics. The daily investment price, IMF component, and residual series of the railway transportation industry are taken as input data. The input data are transmitted through the LSTM model to predict the investment price of the next day. The results show that the EEMD-LSTM model can retain the advantages of EEMD and LSTM and meet the accurate prediction of financial data. The model has good performance for the fitting of actual data and forecast data, and the model has the highest prediction accuracy of 0.2964%. In conclusion, the model proposed is a useful model for predicting financial time series. The exploration can provide an absolute theoretical basis for the formulation and planning of investment risk coping strategies of the railway transportation industry and provide particular theoretical support for the national economic attribute and positioning of the railway transportation industry.



中文翻译:

Emd改进的深度学习模型在铁路运输投资效益和国民经济属性分析中的应用

铁路运输业是促进经济发展的重要因素之一,因此对提高铁路运输业建设规划的投资效益进行了研究,并在此基础上分析了国家紧急状态。提出了基于EEMD-LSTM(整体经验模式分解—长期-短期记忆)模型的铁路运输投资效益和国民经济属性预测模型。EEMD算法用于分解铁路运输行业的每日投资价格,以获得具有不同周期特性的IMF(固有模式函数)。铁路运输行业的每日投资价格,IMF组成部分和残差序列均作为输入数据。输入数据通过LSTM模型传输,以预测第二天的投资价格。结果表明,EEMD-LSTM模型可以保留EEMD和LSTM的优势,并能准确预测财务数据。该模型具有很好的拟合实际数据和预测数据的性能,预测精度最高,为0.2964%。总之,提出的模型是预测财务时间序列的有用模型。该研究可以为铁路运输业的投资风险应对策略的制定和规划提供绝对的理论基础,并为铁路运输业的国民经济属性和定位提供特殊的理论支持。结果表明,EEMD-LSTM模型可以保留EEMD和LSTM的优势,并能准确预测财务数据。该模型具有很好的拟合实际数据和预测数据的性能,预测精度最高,为0.2964%。总之,提出的模型是预测财务时间序列的有用模型。该研究可以为铁路运输业的投资风险应对策略的制定和规划提供绝对的理论基础,并为铁路运输业的国民经济属性和定位提供特殊的理论支持。结果表明,EEMD-LSTM模型可以保留EEMD和LSTM的优势,并能准确预测财务数据。该模型具有很好的拟合实际数据和预测数据的性能,预测精度最高,为0.2964%。总之,提出的模型是预测财务时间序列的有用模型。该研究可以为铁路运输业的投资风险应对策略的制定和规划提供绝对的理论基础,并为铁路运输业的国民经济属性和定位提供特殊的理论支持。该模型的最高预测精度为0.2964%。总之,所提出的模型是预测财务时间序列的有用模型。该研究可以为铁路运输业的投资风险应对策略的制定和规划提供绝对的理论依据,并为铁路运输业的国民经济属性和定位提供特殊的理论支持。该模型的最高预测精度为0.2964%。总之,提出的模型是预测财务时间序列的有用模型。该研究可以为铁路运输业的投资风险应对策略的制定和规划提供绝对的理论依据,并为铁路运输业的国民经济属性和定位提供特殊的理论支持。

更新日期:2021-01-18
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