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 We encourage you to also check out the time series work by the group behind GluonTS, ordered chronographically.
 
 # 2023
+* [Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2307.11494), *Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, Yuyang Wang*, NeurIPS 2023
 * [Learning Physical Models that Can Respect Conservation Laws](https://arxiv.org/pdf/2302.11002.pdf), *Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney*, ICML 2023
 * Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting, *Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park*, ICML 2023
 * [Guiding continuous operator learning through Physics-based boundary constraints](https://arxiv.org/pdf/2212.07477.pdf), *Nadim Saad, Gaurav Gupta, Shima Alizadeh, Danielle C. Maddix*, ICLR 2023