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Box and G.M. Jenkins, \u201cSome recent advances in forecasting and control,\u201d Journal of the Royal Statistical Society: Series C, vol.17, no.2, pp.91-109, 1968. 10.2307\/2985674","DOI":"10.2307\/2985674"},{"key":"2","unstructured":"[3] V. Flunkert, D. Salinas, and J. Gasthaus, \u201cDeepAR: Probabilistic forecasting with autoregressive recurrent networks,\u201d arXiv preprint arXiv: 1704.04110, 2017. 10.48550\/arXiv.1704.04110"},{"key":"3","unstructured":"[4] A. Graves, \u201cGenerating sequences with recurrent neural networks,\u201d arXiv preprint arXiv: 1308.0850, 2013. 10.48550\/arXiv.1308.0850"},{"key":"4","unstructured":"[5] I. Sutskever, O. Vinyals, and Q. V Le, \u201cSequence to sequence learning with neural networks,\u201d Advances in Neural Information Processing Systems, pp.3104-3112, 2014."},{"key":"5","unstructured":"[6] S.S. Rangapuram, M. W Seeger, J. Gasthaus, et al., \u201cDeep state space models for time series forecasting,\u201d Advances in Neural Information Processing Systems, pp.7785-7794, 2018."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[7] G. Lai, W.-C. Chang, Y. Yang, and H. Liu, \u201cModeling long-and short-term temporal patterns with deep neural networks,\u201d 41st International ACM SIGIR Conference on Research &amp; Development in Information Retrieval, pp.95-104, ACM, 2018. 10.1145\/3209978.3210006","DOI":"10.1145\/3209978.3210006"},{"key":"7","unstructured":"[8] R. Yu, S. Zheng, A. Anandkumar, and Y. Yue, \u201cLong-term forecasting using tensor-train rnns,\u201d arXiv preprint arXiv: 1711.00073, 2017. 10.48550\/arXiv.1711.00073"},{"key":"8","unstructured":"[9] D. C Maddix, Y. Wang, and A. Smola, \u201cDeep factors with gaussian processes for forecasting,\u201d arXiv preprint arXiv: 1812.00098, 2018. 10.48550\/arXiv.1812.00098"},{"key":"9","unstructured":"[10] R. Pascanu, T. Mikolov, and Y. Bengio, \u201cOn the difficulty of training recurrent neural networks,\u201d International Conference on Machine Learning, pp.1310-1318, 2013."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[11] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural Computation, vol.9, no.8, pp.1735-1780, 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"11","unstructured":"[12] K. Cho, B. Merri\u00ebnboer, D. Bahdanau, and Y. Bengio, \u201cOn the properties of neural machinetranslation: Encoder-decoder approaches,\u201d arXiv preprint arXiv: 1409.1259, 2014. 10.48550\/arXiv.1409.1259"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[13] U. Khandelwal, H. He, P. Qi, and D. Jurafsky, \u201cSharp nearby, fuzzy far away: How neural language models use context,\u201d arXiv preprint arXiv: 1805.04623, 2018. 10.48550\/arXiv.1805.04623","DOI":"10.18653\/v1\/P18-1027"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[14] A. P Parikh, O. T\u00e4ckstr\u00f6m, D. Das, and J. Uszkoreit, \u201cA decomposable attention model for natural language inference,\u201d arXiv preprint arXiv: 1606.01933, 2016. 10.48550\/arXiv.1606.01933","DOI":"10.18653\/v1\/D16-1244"},{"key":"14","unstructured":"[15] G.E.P Box, G.M. Jenkins, and G.C. Reinsel, Time Series Analysis: Forecasting and Control, 3rd ed., pp.2-45, Prentice Hall, Englewood Cliffs, 1994."},{"key":"15","doi-asserted-by":"publisher","unstructured":"[16] J.N.K. Rao and G. Tintner, \u201cOn the variate difference method,\u201d Australian &amp; New Zealand Journal of Statistics, vol.5, no.3, pp.106-116, 1963. 10.1111\/j.1467-842x.1963.tb00289.x","DOI":"10.1111\/j.1467-842X.1963.tb00289.x"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[17] A. Moradi, M. Alizadeh, M. Samadi, and R. Yusof, \u201cUnderstanding the characteristics of financial time series through neural network and SVM approaches,\u201d International Journal of Electronic Finance, vol.9, no.3, pp.202-216, 2019. 10.1504\/ijef.2019.099045","DOI":"10.1504\/IJEF.2019.099045"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[18] T. Van Gestel, J. Suykens, D.E. Baestaens, A. Lambrechts, G. Lanckriet, B. Vandaele, B. De Moor, and J. Vandewalle, \u201cFinancial time series prediction using least squares support vector machines within the evidence framework,\u201d IEEE Trans. Neural Netw., vol.12, no.4, pp.809-821, 2001. 10.1109\/72.935093","DOI":"10.1109\/72.935093"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[19] M.A. P\u00e9rez-Chavarr\u00eda, H.H. Hidalgo-Silva, and F.J. Ocampo-Torres, \u201cTime series prediction using artificial neural networks,\u201d Ciencias Marinas, vol.28, no.1, pp.67-77, 2002. 10.7773\/cm.v28i1.205","DOI":"10.7773\/cm.v28i1.205"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[20] Y. Bengio and Y. LeCun, \u201cScaling learning algorithms towards AI,\u201d Large-scale Kernel Machines, vol.34, no.5, pp.1-41, 2007.","DOI":"10.7551\/mitpress\/7496.003.0016"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[21] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural Computation, vol.9, no.8, pp.1735-1780, 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[22] G.E. Hinton, S. Osindero, and Y.W. The, \u201cA fast learning algorithm for deep belief nets,\u201d Neural Computation, vol.18, no.7, pp.1527-1554, 2006. 10.1162\/neco.2006.18.7.1527","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"22","unstructured":"[23] S.J. Bai, J.Z. Kolter, and V. Koltun, \u201cAn empirical evaluation of generic convolutional and recurrent networks for sequence modeling,\u201d arXiv: 1803.01271, 2018. 10.48550\/arXiv.1803.01271"},{"key":"23","unstructured":"[24] J. Fan, Q. Li, Y. Zhu, et al., \u201cAspatio-temporal prediction framework for air pollution based on deep RNN,\u201d Science of Surveying and Map, vol.42, no.7, pp.76-83, 2017."},{"key":"24","unstructured":"[25] X. Wang, J. Wu, C. Liu, H. Yang, Y. Du, and W. Niu, \u201cExploring LSTM based recurrent neural network for failure time series prediction,\u201d Journal of Beijing University of Aeronautics and Astronautics, vol.44, no.4, pp.772-784, 2018. 10.13700\/j.bh.1001-5965.2017.0285"},{"key":"25","unstructured":"[26] W. Feng, H. Chen, Z. Zhang, et al., \u201cShort term traffic flow prediction method based on GBRBM-DBN model,\u201d Traffic Information and Safety, vol.36, no.5, pp.99-108, 2018."},{"key":"26","doi-asserted-by":"crossref","unstructured":"[27] B. Yu, H.T. Yin, and Z.Z. Zhu, \u201cSpatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,\u201d Proc. 27th International Joint Conference on Artificial Intelligence, Washington, DC, USA, pp.3634-3640, 2018. 10.24963\/ijcai.2018\/505","DOI":"10.24963\/ijcai.2018\/505"},{"key":"27","unstructured":"[28] A. Aswani, N. Shazeer, N. Parmar, et al., \u201cAttention is all you need,\u201d NeurIPS, 2017."},{"key":"28","unstructured":"[29] S. Wu, X. Xiao, Q. Ding, et al., \u201cAdversarial sparse transformer for time series forecasting,\u201d NeurIPS, 2020."},{"key":"29","unstructured":"[30] J. Devlin, M. Chang, K. Lee, and K. Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d NAACL-HLT, 2019. 10.18653\/v1\/N19-1423"},{"key":"30","unstructured":"[31] T. Brown, B. Mann, N. Ryder, et al., \u201cLanguage modelsare few-shot learners,\u201d NeurIPS, 2020."},{"key":"31","unstructured":"[32] C. Zhi, A. Huang, A. Aswani, et al., \u201cMusic transformer,\u201d ICLR, 2019."},{"key":"32","unstructured":"[33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., \u201cAn image is worth 16x16 words: Transformers for image recognition at scale,\u201d ICLR, 2021."},{"key":"33","doi-asserted-by":"crossref","unstructured":"[34] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, \u201cSwin transformer: Hierarchical vision transformer using shifted windows,\u201d ICCV, 2021. 10.1109\/iccv48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"34","unstructured":"[35] S. Li, X. Jin, Y. Xuan, et al., \u201cEnhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,\u201d NeurIPS, 2019."},{"key":"35","unstructured":"[36] N. Kitaev, L. Kaiser, and A, Levskaya, \u201cReformer: The efficient transformer,\u201d ICLR, 2020."},{"key":"36","doi-asserted-by":"publisher","unstructured":"[37] H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. 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