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Update link to Informer paper #2

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -54,7 +54,7 @@ For general **Recent AI Advances: Tutorials and Surveys in various areas (DL, ML
* TACTiS: Transformer-Attentional Copulas for Time Series, in *ICML* 2022. [\[paper\]](https://arxiv.org/abs/2202.03528)
* Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting, in *ICLR* 2022. [\[paper\]](https://openreview.net/forum?id=0EXmFzUn5I) [\[official code\]](https://github.com/alipay/Pyraformer)
* Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting, in *NeurIPS* 2021. [\[paper\]](https://proceedings.neurips.cc/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html) [\[official code\]](https://github.com/thuml/autoformer)
* Informer: Beyond efficient transformer for long sequence time-series forecasting, in *AAAI* 2021. [\[paper\]](https://www.aaai.org/AAAI21Papers/AAAI-7346.ZhouHaoyi.pdf) [\[official code\]](https://github.com/zhouhaoyi/Informer2020) [\[dataset\]](https://github.com/zhouhaoyi/ETDataset)
* Informer: Beyond efficient transformer for long sequence time-series forecasting, in *AAAI* 2021. [\[paper\]](https://arxiv.org/abs/2012.07436) [\[official code\]](https://github.com/zhouhaoyi/Informer2020) [\[dataset\]](https://github.com/zhouhaoyi/ETDataset)
* Temporal fusion transformers for interpretable multi-horizon time series forecasting, in *International Journal of Forecasting* 2021. [\[paper\]](https://www.sciencedirect.com/science/article/pii/S0169207021000637) [\[code\]](https://github.com/mattsherar/Temporal_Fusion_Transform)
* Probabilistic Transformer For Time Series Analysis, in *NeurIPS* 2021. [\[paper\]](https://proceedings.neurips.cc/paper/2021/hash/c68bd9055776bf38d8fc43c0ed283678-Abstract.html)
* Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case, in *arXiv* 2020. [\[paper\]](https://arxiv.org/abs/2001.08317)
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