Open AccessARIMA-FSVR Hybrid Method for High-Speed Railway Passenger Traffic Forecasting
Author(s) -
Meng Ge,
Junfeng Zhang,
Jinfei Wu,
Houde Han,
Xinghua Shan,
Wang Hongye
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/9961324
Subject(s) - autoregressive integrated moving average , set (abstract data type) , computer science , mean squared prediction error , time series , transport engineering , engineering , data mining , machine learning , programming language
In order to improve the prediction accuracy of railway passenger traffic, an ARIMA model and FSVR are combined to propose a hybrid prediction method. The ARIMA prediction model is established based on the known railway passenger traffic data, and then, the ARIMA prediction results are used as the training set of the FSVR method. At the same time, the air price and historical passenger traffic data are introduced to predict the future passenger traffic, to realize the mixed prediction of railway passenger traffic. The case study demonstrates that the hybrid prediction method can effectively improve the prediction performance of railway passenger traffic. Compared with the single ARIMA method, the hybrid prediction method improves the delay of the prediction results. Compared with the FSVR prediction result, the hybrid prediction method greatly reduces the errors in the extreme points of passenger traffic and long-term prediction. The relevant research results of this paper provide a useful reference for the prediction of railway passenger traffic.