基于奇异谱分析的旅客运输量预测研究

对旅客运输量进行科学准确地预测，可以为交通领域相关部门提供有效的借鉴。将旅客运输量作为研究对象，基于SSA(奇异谱分析)，结合LSTM(长短时记忆神经网络)和ARMA(自回归移动平均模型)，通过SSA降噪处理，将旅客运输量时间序列分解为信号序列和噪声序列，分别对其进行LSTM和ARMA(2,3)建模，预测其变化趋势。通过对比单一的ARIMA(3,1,2)模型和LSTM模型的实验结果表明，SSA-LSTM-ARMA预测旅客运输量效果更好，预测精度更高。

Research on Passenger Traffic Forecast Based on Singular Spectrum Analysis

Scientific and accurate prediction of passenger transport volume can provide effective reference for transportation related departments. Taking passenger transport volume as the research object, based on SSA(singular spectrum analysis ), combined with LSTM(long-short term memory neural network ) and ARMA (auto-regressive moving average model), the time series of passenger transport volume was decomposed into signal sequence and noise sequence through SSA noise reduction processing, and LSTM and ARMA(2,3) modeling were carried out on them respectively. Based on this, its changing trend is predicted. By comparing the experimental results of single ARIMA(3,1,2) model and LSTM model, it shows that SSA-LSTM-ARMA has better prediction effect and higher prediction accuracy in passenger traffic volume.