Abstract
Short-term traffic flow is a variable affected by many factors. Thus, it is quite difficult to forecast accurately with only one model. The ARIMA model and the SVMs model have their own advantages in terms of linearity and nonlinearity. Therefore, making full use of the advantages of ARIMA model and SVMs model to predict traffic flow can significantly improve the overall effect. The current hybrid approach does not take full account of the characteristics of the data, which cause the effect of hybrid model is not always good. In this paper, first of all, we will use time series analysis and feature analysis to find the characteristics of data. Then, based on the analysis results, we decided to use the method of piecewise to fit the data and make the final prediction. The experiment shows that the piecewise hybrid model can give better play to the advantages of the two models.
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Wang, Y., Li, L., Xu, X. (2017). A Piecewise Hybrid of ARIMA and SVMs for Short-Term Traffic Flow Prediction. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_50
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DOI: https://doi.org/10.1007/978-3-319-70139-4_50
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