Medium-and-long-term air traffic flow prediction method based on wavelet transform and grey prediction

Abstract

The invention discloses a medium-and-long-term air traffic flow prediction method based on wavelet transform and grey prediction, and the method comprises the following steps: S1, selecting a waveletbasis function, carrying out the wavelet transform of historical time series data through the selected wavelet basis function, achieving the noise reduction, and reducing the random flow generated byrandom factors; S2, performing grey prediction by using the processed historical data to obtain prediction data; and S3, calculating a signal-to-noise ratio according to the noise reduction data and the historical data, and adding white noise with the same signal-to-noise ratio into the grey prediction data to obtain a final prediction result. According to the invention, a prediction model based on wavelet transform and grey prediction is established to carry out medium-term and long-term prediction on the air traffic flow; firstly, an optimal wavelet basis function is selected; noise reduction processing is carried out on historical time series data through wavelet transformation; random flow generated by random factors is reduced; then grey prediction is carried out through the processedhistorical data; prediction data is obtained; and the prediction precision is effectively improved.

Description

technical field [0001] The invention relates to forecasting of air traffic flow, in particular to a medium and long-term air traffic flow forecasting method based on wavelet transform and gray prediction. Background technique [0002] With the continuous development of my country's economy, my country's air traffic flow is increasing, and air traffic is becoming increasingly busy, which leads to traffic congestion in some areas. The premise of solving these problems lies in the reasonable and accurate forecasting of medium and long-term air traffic flow. Civil aviation planning and flow management workers are conducting in-depth research on the accurate prediction of air traffic flow. [0003] At present, the long-term prediction methods of air traffic flow mainly include regression analysis method, trend method, neural network method, time series method, etc., but there are some defects that affect the prediction accuracy when using these methods for prediction, but the gra...

AI Technical Summary

Problems solved by technology

None of the above forecasting models consider the impact of random factors in medium and long-term air traffic flow forecasting, but only fuzzy random factors. Although the forecasting accuracy is better than regression analysis, trend method, neural network method, time series method, etc., There is still room for improvement in terms of random factors

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