docs: update the docs for Informer;

README.md

@@ -203,6 +203,7 @@ This functionality is implemented with the [Microsoft NNI](https://github.com/mi
 | Neural Net | DLinear | Are Transformers Effective for Time Series Forecasting? [^17] | 2023 |
 | Neural Net | ETSformer | Exponential Smoothing Transformers for Time-series Forecasting [^19] | 2023 |
 | Neural Net | FEDformer | Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [^20] | 2022 |
+| Neural Net | Informer | Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [^21] | 2021 |
 | Neural Net | Autoformer | Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [^15] | 2021 |
 | Neural Net | CSDI | Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [^12] | 2021 |
 | Neural Net | US-GAN | Unsupervised GAN for Multivariate Time Series Imputation [^10] | 2021 |
@@ -332,6 +333,8 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
 [^18]: Nie, Y., Nguyen, N. H., Sinthong, P., & Kalagnanam, J. (2023). [A time series is worth 64 words: Long-term forecasting with transformers](https://openreview.net/forum?id=Jbdc0vTOcol). *ICLR 2023*
 [^19]: Woo, G., Liu, C., Sahoo, D., Kumar, A., & Hoi, S. (2023). [ETSformer: Exponential Smoothing Transformers for Time-series Forecasting](https://openreview.net/forum?id=5m_3whfo483). *ICLR 2023*
 [^20]: Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). [FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting](https://proceedings.mlr.press/v162/zhou22g.html). *ICML 2022*.
+[^21]: Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021). [Informer: Beyond efficient transformer for long sequence time-series forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/17325). *AAAI 2021*.
+
 
 
 <details>

docs/index.rst

@@ -181,6 +181,7 @@ Imputation Neural Net PatchTST (A Time Series is Worth
 Imputation Neural Net DLinear (Are transformers effective for time series forecasting?) 2023 :cite:`zeng2023dlinear`
 Imputation Neural Net ETSformer (Exponential Smoothing Transformers for Time-series Forecasting) 2023 :cite:`woo2023etsformer`
 Imputation Neural Net FEDformer (Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting) 2022 :cite:`zhou2022fedformer`
+Imputation Neural Net Informer (Beyond Efficient Transformer for Long Sequence Time-Series Forecasting) 2021 :cite:`zhou2021informer`
 Imputation Neural Net Autoformer (Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting) 2021 :cite:`wu2021autoformer`
 Imputation Neural Net US-GAN (Unsupervised GAN for Multivariate Time Series Imputation) 2021 :cite:`miao2021SSGAN`
 Imputation Neural Net CSDI (Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation) 2021 :cite:`tashiro2021csdi`

docs/pypots.imputation.rst

@@ -73,6 +73,15 @@ pypots.imputation.fedformer
  :show-inheritance:
  :inherited-members:
 
+pypots.imputation.informer
+------------------------------
+
+.. automodule:: pypots.imputation.informer
+ :members:
+ :undoc-members:
+ :show-inheritance:
+ :inherited-members:
+
 pypots.imputation.autoformer
 ------------------------------
 

docs/references.bib

@@ -544,3 +544,13 @@ @inproceedings{zhou2022fedformer
 pdf = {https://proceedings.mlr.press/v162/zhou22g/zhou22g.pdf},
 url = {https://proceedings.mlr.press/v162/zhou22g.html},
 }
+
+@inproceedings{zhou2021informer,
+title={Informer: Beyond efficient transformer for long sequence time-series forecasting},
+author={Zhou, Haoyi and Zhang, Shanghang and Peng, Jieqi and Zhang, Shuai and Li, Jianxin and Xiong, Hui and Zhang, Wancai},
+booktitle={Proceedings of the AAAI conference on artificial intelligence},
+volume={35},
+number={12},
+pages={11106--11115},
+year={2021}
+}