参考文献

BJC19

Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen. “Invertible residual networks.” 国际机器学习大会. 2019.

BKH16

Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. “Layer normalization.” arXiv preprint arXiv:1607.06450 (2016).

BKK18

Bai, Shaojie, J. Zico Kolter, and Vladlen Koltun. “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling.” arXiv preprint arXiv:1803.01271 (2018).

DDT+16

Du, Nan, et al. “Recurrent Marked Temporal Point Processes: Embedding Event History to Vector.” 第22届 ACM SIGKDD 国际知识发现与数据挖掘大会. ACM, 2016.

GG16

Gal, Yarin, and Zoubin Ghahramani. “A theoretically grounded application of dropout in recurrent neural networks.” 神经信息处理系统进展大会. 2016.

HA21

Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice, 第3版. OTexts, 2021.

KMK16

Krueger, David, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron Courville, and Chris Pal. “Zoneout: Regularizing rnns by randomly preserving hidden activations.” arXiv preprint arXiv:1606.01305 (2016).

LCY+18

Lai, Guokun, et al. “Modeling long-and short-term temporal patterns with deep neural networks.” 第41届 ACM SIGIR 国际信息检索研究与开发大会. ACM, 2018.

LAL+21

Lim, Bryan, Sercan O. Arik, Nicolas Loeff, and Tomas Pfister. “Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting.” 国际预测杂志 37.4 (2021): 1748-1764

SSA20

Makridakis, Spyros, Evangelos Spiliotis, and Vassilios Assimakopoulos. “The M4 Competition: 100,000 time series and 61 forecasting methods.” 国际预测杂志 36.1 (2020): 54-74.

MKH19

Muller, Rafael, Simon Kornblith, and Geoffrey E. Hinton. “When does label smoothing help?.” 神经信息处理系统进展大会. 2019.

MMS17

Merity, Stephen, Bryan McCann, and Richard Socher. “Revisiting activation regularization for language rnns.” arXiv preprint arXiv:1708.01009 (2017).

ODZ+16

Oord, Aaron van den, et al. “Wavenet: A generative model for raw audio.” arXiv preprint arXiv:1609.03499 (2016).

PKC+16

Paine, Tom Le, et al. “Fast wavenet generation algorithm.” arXiv preprint arXiv:1611.09482 (2016).

RSG+18

Rangapuram, Syama Sundar, et al. “Deep state space models for time series forecasting.” 神经信息处理系统进展大会. 2018.

SFG17

Salinas, David, Valentin Flunkert, and Jan Gasthaus. “DeepAR: Probabilistic forecasting with autoregressive recurrent networks.” arXiv preprint arXiv:1704.04110 (2017).

SBG20

Shchur, Oleksandr, et al. “Intensity-free Learning of Temporal Point Processes.” ICLR (国际学习表示大会) 时间点过程研讨会. 2020.

TWJ19

Turkmen, Caner, et al. “Intermittent Demand Forecasting with Deep Renewal Processes.” NeurIPS (神经信息处理系统大会) 时间点过程学习研讨会. 2019.

WTN+17

Wen, Ruofeng, et al. “A multi-horizon quantile recurrent forecaster.” arXiv preprint arXiv:1711.11053 (2017).

YRD15

Yu, Hsiang-Fu, Nikhil Rao, and Inderjit S. Dhillon. “High-dimensional time series prediction with missing values.” arXiv preprint arXiv:1509.08333 (2015).