# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 2 Literature Review
### On Specific Models for Air Traffic Forecasting  
When it comes to the very issue of short-term air traffic forecasting, passenger flow has been covered most in the literature as the passenger service may reflect the service quality of air terminals or airline companies in a more direct way. To be specific, research on an impact factors analysis has come up first; then likewise, the single theory-based forecasting model has been widely used in air traffic forecasting; later on, a comprehensive analysis has been made on all of the possible forecasting models; and finally, the hybrid theory-based forecasting model has also been proposed to address such an issue during recent years. The present available literature on air traffic forecasting can be summarizedas in Figure 1.  
To be specific, [PERSON] (2017) is among the earliest researchers who have pointed out the basic impact factors on air passenger flow, which are economical, technical, and operational ones; later on, [PERSON] (2017) has analyzed the local original-destination (OD) features and found their impact on air passenger flow; [PERSON] and [PERSON] (2018) have added the competition between airlines into consideration; while [PERSON] et al. (2017) have utilized the impact from those various factors to estimate the flight schedules; and, when it comes to the area which has many tourist places of interest, [PERSON] (2018) have made a deep analysis in those impact factors which may just affect the tourist passenger flow.  
Figure 1: Summary of research on air traffic forecasting. SVM: support vector machine; ANN: artificial neural network; EEMD: ensemble empirical mode decomposition.  
Apart from the impact factor analysis, forecasting models have also been built based on different single mathematical theories. [PERSON] [3] has employed an SVM, [PERSON] [4] has used an ANN, [PERSON] et al. [1] have applied a decision tree approach, while [PERSON] and [PERSON] [12] have tried to use Grey theory (GT) to predict short-term air passenger flow.Based on some of the modeling studies above, [PERSON] et al. [36] have introduced fuzzy theory into the decision tree approach and have proposed a fuzzy decision tree so as to build a more accurate model for forecasting; [PERSON] et al. [37] have made some modifications to the GT model and have helped the modified GT model to realize an accurate forecast where fewer data areavailable. [PERSON] [38] has not only employed linear regression in a short-term air passenger flow forecast, but has compared its performance with another model called the classification and regression tree as well. As many models have been built, [PERSON] et al. [2] and [PERSON] et al. [10] have made a comprehensive review of most single theory-based air passenger flow forecasting models.  
Hybrid theory-based forecasting models have only attracted the attention of researchers in air traffic forecasting during recent years, and not so much related work has yet been reported. [PERSON] and [PERSON] [6] happen to be the first group of researchers to employ a hybrid model of ANN and GT for air traffic passenger volume forecasting; while as mentioned above, [PERSON] et al. [30] have tried to use a hybrid EEMD-SVM forecasting model, and have proved that it can outperform the model only based on an SVM. It should be pointed out that even if the hybrid models have already been proved to be better than single theory-based ones, a horizontal performance comparison of the available hybrid theory-based short-term air passenger flow forecasting models has not been reported by researchers at present.