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Statistical methods such as the Box-Jenkins method for time-series forecasting have been prominent since their development in 1970.
T. Edwards, D. Tansley, R. Frank, and N. Davey, “Traffic trends analysis using neural networks,” in Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications , 1997
1997
Earlier work this paper cites.
S. Makridakis and M. Hibon, “The m3-competition: results, conclusions and implications,” 2000
2000
Earlier work this paper cites.
S. D. Balkin and J. K. Ord, “Automatic neural network modeling for univariate time series,” International Journal of Forecasting , 2000
2000
Earlier work this paper cites.
J. J. Commandeur and S. J. Koopman, An introduction to state space time series analysis . Oxford University Press, 2007
2007
Earlier work this paper cites.
D. M. Morens, J. K. Taubenberger, H. A. Harvey, and M. J. Memoli, “The 1918 influenza pandemic: lessons for 2009 and the future,” Critical care medicine , 2010
2010
Earlier work this paper cites.
S. B. Taieb, R. J. Hyndman et al. , Recursive and direct multi-step forecasting: the best of both worlds . Citeseer, 2012
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” arXiv preprint , 2014
2014
Earlier work this paper cites.
X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” Advances in neural information processing systems , 2015
2015
Earlier work this paper cites.
G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control . John Wiley & Sons, 2015
2015
Earlier work this paper cites.
Y. Qin, D. Song, H. Chen, W. Cheng, G. Jiang, and G. Cottrell, “A dual-stage attention-based recurrent neural network for time series prediction,” arXiv preprint , 2017
2017
Earlier work this paper cites.
A. Borovykh, S. Bohte, and C. W. Oosterlee, “Conditional time series forecasting with convolutional neural networks,” arXiv preprint , 2017
2017
Cited alongside, same era.
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The m4 competition: Results, findings, conclusion and way forward,” International Journal of Forecasting , 2018
2018
Cited alongside, same era.
H. Peng, S. U. Bobade, M. E. Cotterell, and J. A. Miller, “Forecasting traffic flow: Short term, long term, and when it rains,” in International Conference on Big Data . Springer, 2018
2018
Cited alongside, same era.
S. S. Rangapuram, M. Seeger, J. Gasthaus, L. Stella, Y. Wang, and T. Januschowski, “Deep state space models for time series forecasting,” in Proceedings of the 32nd international conference on neural information processing systems , 2018
2018
Cited alongside, same era.
L. López and X. Rodo, “A modified seir model to predict the covid-19 outbreak in spain and italy: simulating control scenarios and multi-scale epidemics,” Available at SSRN 3576802 , 2020
2020
Later among the works it cites.
M. S. Shamil, F. Farheen, N. Ibtehaz, I. M. Khan, and M. S. Rahman, “An agent based modeling of covid-19: Validation, analysis, and recommendations,” medRxiv , 2020
2020
Later among the works it cites.
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The m4 competition: 100,000 time series and 61 forecasting methods,” IJF , 2020
2020
Later among the works it cites.
N. Kumar and S. Susan, “Covid-19 pandemic prediction using time series forecasting models,” in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) . IEEE, 2020, pp. 1–7
2020
Later among the works it cites.
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2018
Cited alongside, same era.
H. Peng, N. Klepp, M. Toutiaee, I. B. Arpinar, and J. A. Miller, “Knowledge and situation-aware vehicle traffic forecasting,” in 2019 IEEE International Conference on Big Data (Big Data) . IEEE, 2019
2019
Cited alongside, same era.
C. Fan, Y. Zhang, Y. Pan, X. Li, C. Zhang, R. Yuan, D. Wu, W. Wang, J. Pei, and H. Huang, “Multi-horizon time series forecasting with temporal attention learning,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019
2019
Cited alongside, same era.
S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang, and X. Yan, “Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,” arXiv preprint , 2019
2019
Cited alongside, same era.
B. Lim, S. O. Arik, N. Loeff, and T. Pfister, “Temporal fusion transformers for interpretable multi-horizon time series forecasting,” arXiv preprint , 2019
2019
Cited alongside, same era.
——, “The m5 accuracy competition: Results, findings and conclusions,” Int J Forecast , 2020
2020
Cited alongside, same era.
P. Montero-Manso, G. Athanasopoulos, R. J. Hyndman, and T. S. Talagala, “Fforma: Feature-based forecast model averaging,” International Journal of Forecasting , 2020
2020
Cited alongside, same era.
S. Smyl, “A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting,” IJF
Cited in the paper.
O.-D. Ilie, R.-O. Cojocariu, A. Ciobica, S.-I. Timofte, I. Mavroudis, and B. Doroftei, “Forecasting the spreading of covid-19 across nine countries from europe, asia, and the american continents using the arima models,” Microorganisms , 2020
2020
Later among the works it cites.
S. Prasanth, U. Singh, A. Kumar, V. A. Tikkiwal, and P. H. Chong, “Forecasting spread of covid-19 using google trends: A hybrid gwo-deep learning approach,” Chaos, Solitons & Fractals , 2020
2020
Later among the works it cites.
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The m4 competition: 100,000 time series and 61 forecasting methods,” International Journal of Forecasting , 2020
2020
Later among the works it cites.
“Provisional death counts for coronavirus disease 2019 (covid-19),” Apr 2021. [Online]. Available: https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm
2021
Closest in time.
H. Hewamalage, C. Bergmeir, and K. Bandara, “Recurrent neural networks for time series forecasting: Current status and future directions,” International Journal of Forecasting , 2021
2021
Closest in time.
X. He, K. Zhao, and X. Chu, “Automl: A survey of the state-of-the-art,” Knowledge-Based Systems , 2021
2021
Closest in time.