Abstract
Railway passenger flow forecasting is an important basis for scientific dispatching of railway transportation. In order to remedy the shortcomings of one single time series prediction method for passenger flow, a model of combining autoregressive integrated moving average (ARIMA) with extreme learning machine (ELM) based on wavelet transform, named WAADE is presented in this paper. Firstly, the complex railway passenger flow time series was decomposed into linear and non-linear components by wavelet transform. Then, the decomposed linear and non-linear components were predicted by using ARIMA and ELM respectively. Finally, the final prediction results were obtained through fusing the linear and nonlinear prediction results by wavelet transform once again. At the same time, considering the obvious seasonal and periodic regularity of the railway passenger flow data, a WAADES model was constructed combined the WAADE model with the seasonal model based on the entropy value method. The experimental results show that the prediction accuracy of proposed WAADE and WAADES model is higher than the one of the ARIMA or ELM or seasonal model when used alone. Because of the combination of seasonal characteristics, the prediction accuracy of WAADES model is higher than that of WAADE model. The effectiveness and superiority of the two combined models proposed are proved.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 11862003, 81860635, 11462003), the Key Project of Guangxi Natural Science Foundation (Grant No. 2017GXNSFDA198038), the Project of Guangxi Natural Science Foundation (Grant No. 2018JJA110023), the Project for Promotion of Young and Middle-aged Teachers’ Basic Scientific Research Ability in Guangxi Universities (Grant No. 2019KY0084), Guangxi “Bagui Scholar” Teams for Innovation and Research Project.
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Sha, W., Qiu, S., Yuan, W., Qin, Z. (2019). Hybrid Model of Time Series Prediction Model for Railway Passenger Flow. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_40
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DOI: https://doi.org/10.1007/978-981-15-0121-0_40
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