Predictive Model of Air Transportation Management Based on Intelligent Algorithms of Wireless Network Communication

Due to the numerous factors that affect the air passenger traffic in the air transportation market and the randomness of various factors, in addition, the relationship between it and the air passenger traffic is very complicated, so the air passenger traffic forecast in the air transportation market has always been difficult to solve problem. This research mainly discusses the prediction model of air transportation management based on the intelligent algorithm of wireless network communication. This article uses the wireless network communication intelligent algorithm, comprehensively considers the influence of the GDP growth rate, population growth rate, total import and export volume, and other factors on the air transportation market, and draws a relatively complete forecasting model of aviation business volume. In this paper, we use an equal-weight method, linear combination model method, and Bayesian combination model method when selecting the combination forecasting method (these three methods). Because of the parallelism, robustness, nonlinearity, and other characteristics of the Bayesian network method, it adapts to the complex and highly nonlinear characteristics between air passenger traffic and its influencing factors. In the comprehensive prediction of the single model, the different information contained in the single model is used to achieve different combined prediction effects. The economic information and forecasting angle of the system can reduce systematic forecasting errors and optimize the prognostic results, which can make us more intuitively understand the difference of forecasting results brought by different combination forecasting methods. The Theil inequality coefficient of the ARIMA model is 0.004874, and the average absolute percentage error is 0.005914. This research will play a certain guiding role in the development of China’s civil aviation industry.

An Adaptive Control Combination Forecasting Method for Time Series Data

According to the individual forecasting methods, an adaptive control combination forecasting (ACCF) method with adaptive weighting coefficients was proposed for short-term prediction of the time series data. The US population dataset, the American electric power dataset, and the vibration signal dataset in a hydraulic test rig were separately tested by using ACCF method, and then, the accuracy analysis of ACCF method was carried out in the study. The results showed that, in contrast to individual methods or combination methods, the proposed ACCF method was adaptive to adopt one or some of prediction methods and showed satisfactory forecasting results due to flexible adaptability and a high accuracy. It was also concluded that the higher the noise ratio of the tested datasets, the lower the prediction accuracy of the ACCF method; the ACCF method demonstrated a better prediction trend with good volatility and following quality under noisy data, as compared with other methods.

Tourism Demand Forecasting Based on Grey Model and BP Neural Network

This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision-making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability.