A Chaotic Discriminant Algorithm for Arrival Traffic Flow Time Series Based on Improved Alternative Data Method

A Chaotic Discriminant Algorithm for Arrival Traffic Flow Time Series Based on Improved Alternative Data Method

Chaos discrimination is a prerequisite for the application of chaos theory modeling. Since the average orbital period of an air traffic flow system is long, it is difficult to obtain time series with a small time scale and many data points, so the Small-Data Method is often adopted to quantitatively calculate the chaotic characteristic quantity. However, when using the power spectrum method, it is found that the Small-Data Method is prone to false judgments when the data volume is small. To reduce spurious judgments, we apply a chaos discrimination algorithm based on an Improved Alternative Data Method combined with the Small-Data Method for air traffic flow and analyze it by example. The algorithm was experimentally demonstrated to correct the false judgment results of the Small-Data Method. In particular, when the data volume is only 150, the discrimination accuracy of the improved algorithm is as high as 80%, which is 26% higher than the discrimination accuracy of the Small-Data Method. Moreover, the improved algorithm has better discriminative performance than the Small-Data Method under the same data volume condition, which is suitable for the chaotic discriminative analysis of the arrival traffic flow time series.