scholarly journals Exploring the Effect of COVID-19 on Airline Environmental Efficiency Through an Interval Epsilon-based Measure Model

2021 ◽  
Author(s):  
Ye Li ◽  
Xing-chun Huang ◽  
Qiang Cui
Keyword(s):  
Time Series ◽  
Future Development ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Interval Data ◽  
Environmental Efficiency ◽  
Aviation Industry ◽  
Development Trends ◽  
Recovery Curve ◽  
Measure Model

Abstract COVID-19 has dealt an unprecedented blow to the aviation industry since 2020. This paper applies the Interval Epsilon-Based Measure (IEBM) model to evaluate the optimal quarterly environmental efficiency of 14 global airlines of passenger and cargo subsystems during 2018-2020. Then, the Time Series Prediction method is applied to forecast the interval data of inputs and outputs from 2021 to 2022 and calculate the quarterly efficiency. Thus, the future development trends of airlines can be predicted. Furthermore, the results accord with reality can verify the credibility and accuracy of the model. Furthermore, the results show that: 1. COVID-19 has hit the passenger subsystem harder, while the freight subsystem has become more efficient; 2. The efficiency of the freight subsystem has inevitably declined in the post-epidemic era; 3. Therefore, the airlines will have a “√” shaped recovery curve in the next few years.

A New Time Series Prediction Method Based on LS-SVR and AGO

2013 ◽  
Vol 5 (6) ◽  
pp. 542-551
Author(s):  
Guo Yangming ◽  
Zhang Lu ◽  
Li Xiaolei ◽  
Ran Congbao ◽  
Ma Jiezhong
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Prediction Method ◽  
New Time

GRU-corr Neural Network Optimized by Improved PSO Algorithm for Time Series Prediction

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040010
Author(s):  
Shao-Pei Ji ◽  
Yu-Long Meng ◽  
Liang Yan ◽  
Gui-Shan Dong ◽  
Dong Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Series Data ◽  
Multi Scale ◽  
Network Parameters ◽  
Angle Data

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network

2020 ◽  
Vol 52 (2) ◽  
pp. 1485-1500
Author(s):  
Jiaojiao Hu ◽  
Xiaofeng Wang ◽  
Ying Zhang ◽  
Depeng Zhang ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Prediction ◽  
Prediction Method

A Prediction Method for Nonlinear Correlative Time Series

2010 ◽  
Vol 40-41 ◽  
pp. 930-936 ◽  
Author(s):  
Cong Gui Yuan ◽  
Xin Zheng Zhang ◽  
Shu Qiong Xu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mutual Information ◽  
Orthogonal Polynomial ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Nonlinear Time Series ◽  
Iterative Learning ◽  
Polynomial Basis

A nonlinear correlative time series prediction method is presented in this paper.It is based on the mutual information of time series and orthogonal polynomial basis neural network. Inputs of network are selected by mutual information, and orthogonal polynomial basis is used as active function.The network is trained by an error iterative learning algorithm.This proposed method for nonlinear time series is tested using two well known time series prediction problems:Gas furnace data time series and Mackey-Glass time series.

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