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Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and have consistently ranked among the best entries in forecasting competitions (e.g., M4 and M5). This practical success has further increased the academic interest to understand and improve deep forecasting methods. In this article we provide an introduction and overview of the field: We present important building blocks for deep forecasting in some depth; using these building blocks, we then survey the breadth of the recent deep forecasting literature.\n          <\/jats:p>","DOI":"10.1145\/3533382","type":"journal-article","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T12:53:49Z","timestamp":1652964829000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":269,"title":["Deep Learning for Time Series Forecasting: Tutorial and Literature Survey"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0541-6676","authenticated-orcid":false,"given":"Konstantinos","family":"Benidis","sequence":"first","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9357-0154","authenticated-orcid":false,"given":"Syama Sundar","family":"Rangapuram","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7556-5602","authenticated-orcid":false,"given":"Valentin","family":"Flunkert","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0291-7184","authenticated-orcid":false,"given":"Yuyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Amazon Research, East Palo Alto, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2317-4068","authenticated-orcid":false,"given":"Danielle","family":"Maddix","sequence":"additional","affiliation":[{"name":"Amazon Research, East Palo Alto, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2593-1824","authenticated-orcid":false,"given":"Caner","family":"Turkmen","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2007-773X","authenticated-orcid":false,"given":"Jan","family":"Gasthaus","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4969-2218","authenticated-orcid":false,"given":"Michael","family":"Bohlke-Schneider","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8980-4018","authenticated-orcid":false,"given":"David","family":"Salinas","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8304-7327","authenticated-orcid":false,"given":"Lorenzo","family":"Stella","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-045X","authenticated-orcid":false,"given":"Fran\u00e7ois-Xavier","family":"Aubet","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0756-8120","authenticated-orcid":false,"given":"Laurent","family":"Callot","sequence":"additional","affiliation":[{"name":"Amazon Research, Charlottenstrasse, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6475-1626","authenticated-orcid":false,"given":"Tim","family":"Januschowski","sequence":"additional","affiliation":[{"name":"Zalando SE, Berlin, Germany"}]}],"member":"320","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"265","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation","author":"Abadi Martin","year":"2016","unstructured":"Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. 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Association for Computational Linguistics, 4171\u20134186."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2018.09.007"},{"key":"e_1_3_2_47_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"Dinh Laurent","year":"2017","unstructured":"Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2017. Density estimation using Real NVP. In Proceedings of the 5th International Conference on Learning Representations."},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.5555\/2832581.2832731"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939875"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199641178.001.0001"},{"key":"e_1_3_2_51_2","unstructured":"Elena Ehrlich Laurent Callot and Fran\u00e7ois-Xavier Aubet. 2021. Spliced binned-pareto distribution for robust modeling of heavy-tailed time series. arXiv:2106.10952. 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Retrieved from https:\/\/arxiv.org\/abs\/1706.02633."},{"key":"e_1_3_2_55_2","article-title":"Forecasting: Theory and practice","author":"al. Fotios Petropoulos et","year":"2020","unstructured":"Fotios Petropoulos et al.2020. Forecasting: Theory and practice. International Journal of Forecasting (2020).","journal-title":"International Journal of Forecasting"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3332289"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3383118"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3229878"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3314033"},{"key":"e_1_3_2_60_2","first-page":"1367","volume-title":"Proceedings of the IEEE International Conference on Big Data","author":"Fawaz Hassan Ismail","year":"2018","unstructured":"Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2018. 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Multivariate time series forecasting with latent graph inference. arXiv preprint (2022).","journal-title":"arXiv preprint"},{"key":"e_1_3_2_65_2","first-page":"1901","volume-title":"Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics","author":"Gasthaus Jan","year":"2019","unstructured":"Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski. 2019. Probabilistic forecasting with spline quantile function RNNs. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. 1901\u20131910."},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1201\/b16018"},{"key":"e_1_3_2_67_2","article-title":"The dynamic factor analysis of economic time series","author":"Geweke John","year":"1977","unstructured":"John Geweke. 1977. The dynamic factor analysis of economic time series. 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Retrieved from https:\/\/arxiv.org\/abs\/2107.03743."},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2007.01.007"},{"key":"e_1_3_2_75_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Hasson Hilaf","year":"2021","unstructured":"Hilaf Hasson, Bernie Wang, Tim Januschowski, and Jan Gasthaus. 2021. Probabilistic forecasting: A level-set approach. In Proceedings of the Advances in Neural Information Processing Systems. 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In Proceedings of the Advances in Neural Information Processing Systems. 6754\u20136764."},{"key":"e_1_3_2_135_2","unstructured":"Olof Mogren. 2016. C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv:1611.09904. Retrieved from https:\/\/arxiv.org\/abs\/1611.09904."},{"key":"e_1_3_2_136_2","unstructured":"Pablo Montero-Manso and Rob J. Hyndman. 2020. Principles and algorithms for forecasting groups of time series: Locality and globality. arXiv:2008.00444. Retrieved from https:\/\/arxiv.org\/abs\/2008.00444."},{"key":"e_1_3_2_137_2","unstructured":"Srayanta Mukherjee Devashish Shankar Atin Ghosh Nilam Tathawadekar Pramod Kompalli Sunita Sarawagi and Krishnendu Chaudhury. 2018. ARMDN: Associative and recurrent mixture density networks for eretail demand forecasting. arXiv:1803.03800. Retrieved from https:\/\/arxiv.org\/abs\/1803.03800."},{"key":"e_1_3_2_138_2","first-page":"3898","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Oliva Junier","year":"2018","unstructured":"Junier Oliva, Avinava Dubey, Manzil Zaheer, Barnabas Poczos, Ruslan Salakhutdinov, Eric Xing, and Jeff Schneider. 2018. Transformation autoregressive networks. In Proceedings of the International Conference on Machine Learning. PMLR, 3898\u20133907."},{"key":"e_1_3_2_139_2","unstructured":"Boris N. Oreshkin Dmitri Carpov Nicolas Chapados and Yoshua Bengio. 2019. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv:1905.10437. Retrieved from https:\/\/arxiv.org\/abs\/1905.10437."},{"key":"e_1_3_2_140_2","unstructured":"Boris N. Oreshkin Dmitri Carpov Nicolas Chapados and Yoshua Bengio. 2020. Meta-learning framework with applications to zero-shot time-series forecasting. arXiv:2002.02887. Retrieved from https:\/\/arxiv.org\/abs\/2002.02887."},{"key":"e_1_3_2_141_2","unstructured":"George Papamakarios Theo Pavlakou and Iain Murray. 2017. Masked autoregressive flow for density estimation. arXiv:1705.07057. Retrieved from https:\/\/arxiv.org\/abs\/1705.07057."},{"key":"e_1_3_2_142_2","volume-title":"Proceedings of the 25th International Conference on Artificial Intelligence and Statistics","author":"Park Youngsuk","year":"2022","unstructured":"Youngsuk Park, Danielle Maddix, Fran\u00e7ois-Xavier Aubet, Kelvin Kan, Jan Gasthaus, and Yuyang Wang. 2022. Learning quantile functions without quantile crossing for distribution-free time series forecasting. 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Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1109\/CIEL.2014.7015739"},{"key":"e_1_3_2_146_2","unstructured":"Stephan Rabanser Tim Januschowski Valentin Flunkert David Salinas and Jan Gasthaus. 2020. The effectiveness of discretization in forecasting: An empirical study on neural time series models. arXiv:2005.10111. Retrieved from https:\/\/arxiv.org\/abs\/2005.10111."},{"key":"e_1_3_2_147_2","first-page":"7785","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Rangapuram Syama Sundar","year":"2018","unstructured":"Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018. Deep state space models for time series forecasting. In Proceedings of the Advances in Neural Information Processing Systems. 7785\u20137794."},{"key":"e_1_3_2_148_2","unstructured":"Syama Sundar Rangapuram Shubham Shubham Kapoor Rajbir Nirwan Pedro Mercado Tim Januschowski Yuyang Wang and Michael Bohlke-Schneider. 2022. Coherent Probabilistic Forecasting for Temporal Hierarchies. (2022)."},{"key":"e_1_3_2_149_2","first-page":"8832","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Rangapuram Syama Sundar","year":"2021","unstructured":"Syama Sundar Rangapuram, Lucien D. Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, and Tim Januschowski. 2021. End-to-end learning of coherent probabilistic forecasts for hierarchical time series. In Proceedings of the International Conference on Machine Learning. PMLR, 8832\u20138843."},{"key":"e_1_3_2_150_2","volume-title":"Gaussian Process for Machine Learning","author":"Rasmussen Carl Edward","year":"2006","unstructured":"Carl Edward Rasmussen and Christopher K. I. Williams. 2006. Gaussian Process for Machine Learning. MIT press."},{"key":"e_1_3_2_151_2","first-page":"8857","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Rasul Kashif","year":"2021","unstructured":"Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf. 2021. Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In Proceedings of the International Conference on Machine Learning. PMLR, 8857\u20138868."},{"key":"e_1_3_2_152_2","unstructured":"Kashif Rasul Abdul-Saboor Sheikh Ingmar Schuster Urs Bergmann and Roland Vollgraf. 2020. Multi-variate probabilistic time series forecasting via conditioned normalizing flows. arXiv:2002.06103. Retrieved from https:\/\/arxiv.org\/abs\/2002.06103."},{"key":"e_1_3_2_153_2","volume-title":"The Perceptron, A Perceiving and Recognizing Automaton Project Para","author":"Rosenblatt Frank","year":"1957","unstructured":"Frank Rosenblatt. 1957. The Perceptron, A Perceiving and Recognizing Automaton Project Para. Cornell Aeronautical Laboratory."},{"key":"e_1_3_2_154_2","doi-asserted-by":"publisher","DOI":"10.21236\/ADA164453"},{"key":"e_1_3_2_155_2","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_3_2_156_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Salinas David","year":"2019","unstructured":"David Salinas, Michael Bohlke-Schneider, Laurent Callot, and Jan Gasthaus. 2019. High-dimensional multivariate forecasting with low-rank gaussian copula processes. 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In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_165_2","first-page":"10919","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Sharma Anuj","year":"2018","unstructured":"Anuj Sharma, Robert Johnson, Florian Engert, and Scott Linderman. 2018. Point process latent variable models of larval zebrafish behavior. In Proceedings of the Advances in Neural Information Processing Systems. 10919\u201310930."},{"key":"e_1_3_2_166_2","article-title":"Detecting anomalous event sequences with temporal point processes","volume":"34","author":"Shchur Oleksandr","year":"2021","unstructured":"Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, and Stephan G\u00fcnnemann. 2021. Detecting anomalous event sequences with temporal point processes. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_167_2","doi-asserted-by":"crossref","unstructured":"Oleksandr Shchur Ali Caner T\u00fcrkmen Tim Januschowski and Stephan G\u00fcnnemann. 2021. Neural temporal point processes: A review. arXiv:2104.03528. 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Retrieved from https:\/\/arxiv.org\/abs\/1911.10416."},{"key":"e_1_3_2_183_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-46147-8_28"},{"key":"e_1_3_2_184_2","article-title":"WaveNet: A generative model for raw audio","volume":"125","author":"Oord A\u00e4ron Van Den","year":"2016","unstructured":"A\u00e4ron Van Den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W. Senior, and Koray Kavukcuoglu. 2016. WaveNet: A generative model for raw audio. SSW 125 (2016).","journal-title":"SSW"},{"key":"e_1_3_2_185_2","first-page":"5998","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. 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Retrieved from https:\/\/arxiv.org\/abs\/1703.08524."},{"key":"e_1_3_2_197_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.08.003"},{"key":"e_1_3_2_198_2","article-title":"Parsimonious quantile regression of financial asset tail dynamics via sequential learning","volume":"31","author":"Yan Xing","year":"2018","unstructured":"Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, and Qi Wu. 2018. Parsimonious quantile regression of financial asset tail dynamics via sequential learning. Advances in Neural Information Processing Systems 31 (2018).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_199_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.60"},{"key":"e_1_3_2_200_2","article-title":"Time-series generative adversarial networks","volume":"32","author":"Yoon Jinsung","year":"2019","unstructured":"Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019. Time-series generative adversarial networks. 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