Deep Learning Time Series Forecasting Paper List

Reading list of deep learning time series forecasting

Review & Competition

• Längkvist M, Karlsson L, Loutfi A. A review of unsupervised feature learning and deep learning for time-series modeling[J]. Pattern Recognition Letters, 2014, 42: 11-24.
• Taieb S B, Atiya A F. A bias and variance analysis for multistep-ahead time series forecasting[J]. IEEE transactions on neural networks and learning systems, 2015, 27(1): 62-76.
• Bianchi F M, Maiorino E, Kampffmeyer M C, et al. An overview and comparative analysis of recurrent neural networks for short term load forecasting[J]. arXiv preprint arXiv:1705.04378, 2017.
• Lim B, Zohren S. Time Series Forecasting With Deep Learning: A Survey[J]. arXiv preprint arXiv:2004.13408, 2020.
• Makridakis S, Spiliotis E, Assimakopoulos V. The M4 Competition: 100,000 time series and 61 forecasting methods[J]. International Journal of Forecasting, 2020, 36(1): 54-74.

RNN & CNN

• Borovykh A, Bohte S, Oosterlee C W. Conditional time series forecasting with convolutional neural networks[J]. arXiv preprint arXiv:1703.04691, 2017.
• Wen R, Torkkola K, Narayanaswamy B, et al. A multi-horizon quantile recurrent forecaster[J]. arXiv preprint arXiv:1711.11053, 2017.
• Bai S, Kolter J Z, Koltun V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[J]. arXiv preprint arXiv:1803.01271, 2018.
• Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions[J]. European Journal of Operational Research, 2018, 270(2): 654-669.
• Lim B, Zohren S, Roberts S. Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction[J]. arXiv preprint arXiv:1901.08096, 2019.

Attention & Interpertibility

• Choi E, Bahadori M T, Sun J, et al. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism[C]//Advances in Neural Information Processing Systems. 2016: 3504-3512.
• Cinar Y G, Mirisaee H, Goswami P, et al. Position-based content attention for time series forecasting with sequence-to-sequence rnns[C]//International conference on neural information processing. Springer, Cham, 2017: 533-544.
• Fan C, Zhang Y, Pan Y, et al. Multi-horizon time series forecasting with temporal attention learning[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 2527-2535.
• Li S, Jin X, Xuan Y, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting[C]//Advances in Neural Information Processing Systems. 2019: 5243-5253.
• Lim B, Arik S O, Loeff N, et al. Temporal fusion transformers for interpretable multi-horizon time series forecasting[J]. arXiv preprint arXiv:1912.09363, 2019.
• Alaa A M, van der Schaar M. Attentive state-space modeling of disease progression[C]//Advances in Neural Information Processing Systems. 2019: 11338-11348.

Hybrid Method

• Grover A, Kapoor A, Horvitz E. A deep hybrid model for weather forecasting[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: 379-386.
• Lai G, Chang W C, Yang Y, et al. Modeling long-and short-term temporal patterns with deep neural networks[C]//The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018: 95-104.
• Rangapuram S S, Seeger M W, Gasthaus J, et al. Deep state space models for time series forecasting[C]//Advances in neural information processing systems. 2018: 7785-7794.
• Salinas D, Flunkert V, Gasthaus J, et al. DeepAR: Probabilistic forecasting with autoregressive recurrent networks[J]. International Journal of Forecasting, 2019.
• Wang Y, Smola A, Maddix D C, et al. Deep factors for forecasting[J]. arXiv preprint arXiv:1905.12417, 2019.
• Smyl S. A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting[J]. International Journal of Forecasting, 2020, 36(1): 75-85.