## 5 Discussion and Conclusions
(2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Geoman: Multi-level attention networks for geo-sensory time series prediction. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 13-19 July 2018; Volume 2018, pp. 3428-3434. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January-1 February 2019; Volume 33, No. 1, pp. 922-929. * [PERSON] and [PERSON] (2022) [PERSON]; [PERSON] [PERSON] Adaptive Graph Spatial-Temporal Transformer Network for Traffic Forecasting. In Proceedings of the 31 st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, 17-21 October 2022; pp. 3933-3937. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Spatio-Temporal Memory Augmented Multi-Level Attention Network for Traffic Prediction. _IEEE Trans. Knowl. Data Eng._**2023**, _36_, 2643-2658. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Representing long-range context for graph neural networks with global attention. _Adv. Neural Inf. Process. Syst._**2021**, _34_, 13266-13279. * [PERSON] et al. (2022) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Understanding over-squashing and bottlenecks on graphs via curvature. In Proceedings of the International Conference on Learning Representations, Virtual, 25-29 April 2022. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Convolutional neural networks on graphs with chebyshev approximation, revisited. _Adv. Neural Inf. Process. Syst._**2022**, _35_, 7264-7276. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. _Adv. Neural Inf. Process. Syst._**2021**, _34_, 22419-22430. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. _Adv. Neural Inf. Process. Syst._**2019**, _32_, 5243-5253. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. Universal Language Model Fine-tuning for Text Classification. _arXiv_**2018**, arXiv:1803.01271. * [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA, 7-9 May 2015; Volume 5, p. 6. * [PERSON] and [PERSON] (1997) [PERSON]; [PERSON] [PERSON] Long short-term memory. _Neural Comput._**1997**, 9, 1735-1780. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Timesnet: Temporal 2D-variation modeling for general time series analysis. In Proceedings of the Eleventh International Conference on Learning Representations, Virtual, 25-29 April 2022. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] Convolutional neural networks on graphs with fast localized spectral filtering. _Adv. Neural Inf. Process. Syst._**2016**, _29_, 3844-3852. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] Graph neural controlled differential equations for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Online, 22 February-1 March 2022; Volume 36, No. 6, pp. 6367-6374. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. _IEEE Trans. Knowl. Data Eng._**2021**, _34_, 5415-5428. [CrossRef]  
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