Abstract
Abstract In order to analyze the characteristics of airport flight delayed time series, based on the construction of flight delay time series, firstly, the K-means algorithm is used to cluster the time series of delayed departures. Secondly, combining with R/S analysis method of Fractal theory, Hurst index of the series is calculated, and Fractal characteristics of the series are analyzed. Then, the VAR (Vector Auto Regression) model is constructed, and Impulse Response Function (IRF) and Variance Decomposition are conducted to explore the impact of the fluctuation of flight delay time series on the future delay. The results show that K-means algorithm divides the time series into five categories, and each category has significant characteristics. Hurst index values of different time series are in the interval of (0.5, 1), indicating that the time series have good fractal characteristics. Through the IRF and Variance Decomposition of VAR model, results show that the time series are significantly affected by random pulses, and the prediction changes of the series come from multiple time series fluctuations. The prediction results show that the flight delay time series is predictable.
Highlights
For solving the problem of flight delay and improving the punctuality rate, domestic and foreign scholars have studied the influencing factors of flight delay [1], large-scale flight delay [2], flight delay propagation [3], flight delay prediction [4], etc
In order to analyze the characteristics of airport flight delayed time series, based on the construction of flight delay time series, firstly, the K-means algorithm is used to cluster the time series of delayed departures
The VAR (Vector Auto Regression) model is constructed, and Impulse Response Function (IRF) and Variance Decomposition are conducted to explore the impact of the fluctuation of flight delay time series on the future delay
Summary
For solving the problem of flight delay and improving the punctuality rate, domestic and foreign scholars have studied the influencing factors of flight delay [1], large-scale flight delay [2], flight delay propagation [3], flight delay prediction [4], etc. Most of them explore the nature of flight delay through the method of establishing an analysis model by integrating the influencing factors of flight delay, but the trend is to use the method of time series to analyze the condition of flight delay. Exploring the characteristics of flight delay time series can provide strong support for the analysis and prediction of flight delay. Based on the clustering center, the series I are divided into five categories, and the delay degree of each category is analyzed respectively. (ii) The fractal characteristics of time series are explored by using the fractal theory.
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