Research on Floating Car Speed Short-Time Prediction with Wavelet-ARIMA Under Data Missing

摘要

Aiming at the problem of predicting the effect of floating car speed prediction due to missing data and noise disturbance, in this chapter, the accuracy of 5, 10, 20, 30% of the regression filling method, EM method, PMM method to fill the accuracy of the analysis, while using wavelet transform strong time domain and frequency domain resolution characteristics, and the original data is denoised by the translation invariant wavelet transform, combined with the Auto-Regressive Moving Average Model (ARIMA) in terms of time series prediction, a wavelet-ARIMA algorithm for predicting vehicle speed is proposed. The experimental results show that with the increase of the sample data loss rate, the error of the three padding algorithms increases, but the PMM error curve is more gentle. Compared with the un-denoised ARIMA model, the Wavelet-ARIMA model is more accurate for predicting the speed of the floating car.