Abstract
Due to the complexity of the traffic system and the constantly changing characteristics of many influencing factors, long-term traffic forecasting is extremely challenging. Many existing methods based on deep learning perform well in short-term prediction, but do not perform well in Long-Term Time Series Forecasting (LTSF) tasks. These existing methods are difficult to capture the dependencies of long-term temporal sequences. To overcome these limitations, this paper introduces a new graph neural network architecture for spatial-temporal graph modeling. By using simple graph convolutional networks and developing novel spatial-temporal adaptive dependency matrices, our model can capture the hidden spatial-temporal internal dependency in the data. At the same time, we add external dependency to the model. We utilize the periodicity between long-term time series and historical data and introduce a Historical Attention Mechanism to capture historical dependencies in combination with historical data, which can expand the receptive field of the model from local relationships to historical relationships to help improve the prediction accuracy and avoid the problem of too long sequence and too much useless information caused by taking the entire historical sequence as input. Experimental results on two public traffic datasets, NYC-TLC and England-Highways, demonstrate the superior performance of our method.
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References
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Du, B., et al.: Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans. Intell. Transp. Syst. 21(3), 972–985 (2019)
Hamilton, J.D.: Time Series Analysis. Princeton University Press, Princeton (2020)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Ma, X., Zhang, J., Du, B., Ding, C., Sun, L.: Parallel architecture of convolutional bi-directional LSTM neural networks for network-wide metro ridership prediction. IEEE Trans. Intell. Transp. Syst. 20(6), 2278–2288 (2018)
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 753–763 (2020)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
Ye, J., Sun, L., Du, B., Fu, Y., Tong, X., Xiong, H.: Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 305–313 (2019)
Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021)
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: Fedformer: frequency enhanced decomposed transformer for long-term series forecasting. In: International Conference on Machine Learning, pp. 27268–27286. PMLR (2022)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (62272023, 51991391, 51991395).
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Wang, Z., Xu, Y., Han, L., Zhu, T., Sun, L. (2023). Multivariate Long-Term Traffic Forecasting with Graph Convolutional Network and Historical Attention Mechanism. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14120. Springer, Cham. https://doi.org/10.1007/978-3-031-40292-0_10
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DOI: https://doi.org/10.1007/978-3-031-40292-0_10
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