Update docs and references (#410)

* docs: rename issue label "new model" to "new algo"; * docs: update docs and references;
WenjieDu · May 13, 2024 · 7127081 · 7127081
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diff --git a/.github/ISSUE_TEMPLATE/model-addition.yml b/.github/ISSUE_TEMPLATE/model-addition.yml
@@ -1,6 +1,6 @@
 name: "🤙 New Model Addition"
 description: Submit a request to implement a new model.
-labels: ["new model", "enhancement"]
+labels: ["new algo", "enhancement"]
 
 body:
  - type: textarea

diff --git a/README.md b/README.md
@@ -53,7 +53,7 @@
   <img alt="arXiv DOI" src="https://img.shields.io/badge/DOI-10.48550/arXiv.2305.18811-F8F7F0">
  </a>
  <a href="https://github.com/WenjieDu/PyPOTS/blob/main/README_zh.md">
-  <img alt="README in Chinese" src="https://img.shields.io/badge/README-%F0%9F%87%A8%F0%9F%87%B3中文版-FCEFE8">
+  <img alt="README in Chinese" src="https://pypots.com/figs/pypots_logos/readme/CN.svg">
  </a>
 </p>
 
@@ -86,18 +86,18 @@ The rest of this readme file is organized as follows:
 ## ❖ Available Algorithms
 PyPOTS supports imputation, classification, clustering, forecasting, and anomaly detection tasks on multivariate partially-observed
 time series with missing values. The table below shows the availability of each algorithm (sorted by Year) in PyPOTS for different tasks.
-The symbol ✅ indicates the algorithm is available for the corresponding task (note that models will be continuously updated
+The symbol `✅` indicates the algorithm is available for the corresponding task (note that models will be continuously updated
 in the future to handle tasks that are not currently supported. Stay tuned❗️).
 
 🌟 Since **v0.2**, all neural-network models in PyPOTS has got hyperparameter-optimization support.
 This functionality is implemented with the [Microsoft NNI](https://github.com/microsoft/nni) framework. You may want to refer to our time-series
 imputation survey repo [Awesome_Imputation](https://github.com/WenjieDu/Awesome_Imputation) to see how to config and
 tune the hyperparameters.
 
-🔥 Note that Transformer, iTransformer, FreTS, Crossformer, PatchTST, DLinear, ETSformer, Pyraformer, Nonstationary Transformer, FiLM, FEDformer, Informer, Autoformer
-are not proposed as imputation methods in their original papers, and they cannot accept POTS as input.
-**To make them applicable on POTS data, we apply the embedding strategy and training approach (ORT+MIT)
-the same as we did in [SAITS paper](https://arxiv.org/pdf/2202.08516).**
+🔥 Note that all models whose name with `🧑‍🔧` in the table (e.g. Transformer, iTransformer, Informer etc.) are not originally
+proposed as algorithms for POTS data in their papers, and they cannot directly accept time series with missing values as input,
+let alone imputation. **To make them applicable to POTS data, we specifically apply the embedding strategy and
+training approach (ORT+MIT) the same as we did in [SAITS paper](https://arxiv.org/pdf/2202.08516).**
 
 The task types are abbreviated as follows:
 **`IMPU`**: Imputation;
@@ -107,36 +107,41 @@ The task types are abbreviated as follows:
 **`ANOD`**: Anomaly Detection.
 The paper references and links are all listed at the bottom of this file.
 
-| **Type** | **Algo** | **IMPU** | **FORE** | **CLAS** | **CLUS** | **ANOD** | **Year - Venue** |
-|:--------------|:-----------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:-------------------|
-| Neural Net | iTransformer[^24] | ✅ | | | | | `2024 - ICLR` |
-| Neural Net | SAITS[^1] | ✅ | | | | | `2023 - ESWA` |
-| Neural Net | FreTS[^23] | ✅ | | | | | `2023 - NeurIPS` |
-| Neural Net | Crossformer[^16] | ✅ | | | | | `2023 - ICLR` |
-| Neural Net | TimesNet[^14] | ✅ | | | | | `2023 - ICLR` |
-| Neural Net | PatchTST[^18] | ✅ | | | | | `2023 - ICLR` |
-| Neural Net | ETSformer[^19] | ✅ | | | | | `2023 - ICLR` |
-| Neural Net | DLinear[^17] | ✅ | | | | | `2023 - AAAI` |
-| Neural Net | Nonstationary <br>Transformer[^25] | ✅ | | | | | `2022 - NeurIPS` |
-| Neural Net | FiLM[^22] | ✅ | | | | | `2022 - NeurIPS` |
-| Neural Net | Pyraformer[^26] | | | ✅ | | | `2022 - ICLR` |
-| Neural Net | Raindrop[^5] | | | ✅ | | | `2022 - ICLR` |
-| Neural Net | FEDformer[^20] | ✅ | | | | | `2022 - ICML` |
-| Neural Net | Autoformer[^15] | ✅ | | | | | `2021 - NeurIPS` |
-| Neural Net | CSDI[^12] | ✅ | ✅ | | | | `2021 - NeurIPS` |
-| Neural Net | Informer[^21] | ✅ | | | | | `2021 - AAAI` |
-| Neural Net | US-GAN[^10] | ✅ | | | | | `2021 - AAAI` |
-| Neural Net | CRLI[^6] | | | | ✅ | | `2021 - AAAI` |
-| Probabilistic | BTTF[^8] | | ✅ | | | | `2021 - TPAMI` |
-| Neural Net | GP-VAE[^11] | ✅ | | | | | `2020 - AISTATS` |
-| Neural Net | VaDER[^7] | | | | ✅ | | `2019 - GigaSci.` |
-| Neural Net | M-RNN[^9] | ✅ | | | | | `2019 - TBME` |
-| Neural Net | BRITS[^3] | ✅ | | ✅ | | | `2018 - NeurIPS` |
-| Neural Net | GRU-D[^4] | ✅ | | ✅ | | | `2018 - Sci. Rep.` |
-| Neural Net | Transformer[^2] | ✅ | | | | | `2017 - NeurIPS` |
-| Naive | LOCF/NOCB | ✅ | | | | | |
-| Naive | Mean | ✅ | | | | | |
-| Naive | Median | ✅ | | | | | |
+| **Type** | **Algo** | **IMPU** | **FORE** | **CLAS** | **CLUS** | **ANOD** | **Year - Venue** |
+|:--------------|:----------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:-------------------|
+| Neural Net | iTransformer🧑‍🔧[^24] | ✅ | | | | | `2024 - ICLR` |
+| Neural Net | SAITS[^1] | ✅ | | | | | `2023 - ESWA` |
+| Neural Net | FreTS🧑‍🔧[^23] | ✅ | | | | | `2023 - NeurIPS` |
+| Neural Net | Koopa🧑‍🔧[^29] | ✅ | | | | | `2023 - NeurIPS` |
+| Neural Net | Crossformer🧑‍🔧[^16] | ✅ | | | | | `2023 - ICLR` |
+| Neural Net | TimesNet[^14] | ✅ | | | | | `2023 - ICLR` |
+| Neural Net | PatchTST🧑‍🔧[^18] | ✅ | | | | | `2023 - ICLR` |
+| Neural Net | ETSformer🧑‍🔧[^19] | ✅ | | | | | `2023 - ICLR` |
+| Neural Net | MICN🧑‍🔧[^27] | ✅ | | | | | `2023 - ICLR` |
+| Neural Net | DLinear🧑‍🔧[^17] | ✅ | | | | | `2023 - AAAI` |
+| Neural Net | TiDE🧑‍🔧[^28] | ✅ | | | | | `2023 - TMLR` |
+| Neural Net | SCINet🧑‍🔧[^30] | ✅ | | | | | `2022 - NeurIPS` |
+| Neural Net | Nonstationary Tr.🧑‍🔧[^25] | ✅ | | | | | `2022 - NeurIPS` |
+| Neural Net | FiLM🧑‍🔧[^22] | ✅ | | | | | `2022 - NeurIPS` |
+| Neural Net | RevIN_SCInet🧑‍🔧[^31] | ✅ | | | | | `2022 - ICLR` |
+| Neural Net | Pyraformer🧑‍🔧[^26] | ✅ | | | | | `2022 - ICLR` |
+| Neural Net | Raindrop[^5] | | | ✅ | | | `2022 - ICLR` |
+| Neural Net | FEDformer🧑‍🔧[^20] | ✅ | | | | | `2022 - ICML` |
+| Neural Net | Autoformer🧑‍🔧[^15] | ✅ | | | | | `2021 - NeurIPS` |
+| Neural Net | CSDI[^12] | ✅ | ✅ | | | | `2021 - NeurIPS` |
+| Neural Net | Informer🧑‍🔧[^21] | ✅ | | | | | `2021 - AAAI` |
+| Neural Net | US-GAN[^10] | ✅ | | | | | `2021 - AAAI` |
+| Neural Net | CRLI[^6] | | | | ✅ | | `2021 - AAAI` |
+| Probabilistic | BTTF[^8] | | ✅ | | | | `2021 - TPAMI` |
+| Neural Net | GP-VAE[^11] | ✅ | | | | | `2020 - AISTATS` |
+| Neural Net | VaDER[^7] | | | | ✅ | | `2019 - GigaSci.` |
+| Neural Net | M-RNN[^9] | ✅ | | | | | `2019 - TBME` |
+| Neural Net | BRITS[^3] | ✅ | | ✅ | | | `2018 - NeurIPS` |
+| Neural Net | GRU-D[^4] | ✅ | | ✅ | | | `2018 - Sci. Rep.` |
+| Neural Net | Transformer🧑‍🔧[^2] | ✅ | | | | | `2017 - NeurIPS` |
+| Naive | LOCF/NOCB | ✅ | | | | | |
+| Naive | Mean | ✅ | | | | | |
+| Naive | Median | ✅ | | | | | |
 
 
 ## ❖ PyPOTS Ecosystem
@@ -356,7 +361,11 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
 [^24]: Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., & Long, M. (2024). [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://openreview.net/forum?id=JePfAI8fah). *ICLR 2024*.
 [^25]: Liu, Y., Wu, H., Wang, J., & Long, M. (2022). [Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting](https://proceedings.neurips.cc/paper_files/paper/2022/hash/4054556fcaa934b0bf76da52cf4f92cb-Abstract-Conference.html). *NeurIPS 2022*.
 [^26]: Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A. X., & Dustdar, S. (2022). [Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting](https://openreview.net/forum?id=0EXmFzUn5I). *ICLR 2022*.
-
+[^27]: Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., & Xiao, Y. (2023). [MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting](https://openreview.net/forum?id=zt53IDUR1U). *ICLR 2023*.
+[^28]: Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., & Yu, R. (2023). [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://openreview.net/forum?id=pCbC3aQB5W). *TMLR 2023*.
+[^29]: Liu, Y., Li, C., Wang, J., & Long, M. (2023). [Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors](https://proceedings.neurips.cc/paper_files/paper/2023/hash/28b3dc0970fa4624a63278a4268de997-Abstract-Conference.html). *NeurIPS 2023*.
+[^30]: Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., & Xu, Q. (2022). [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://proceedings.neurips.cc/paper_files/paper/2022/hash/266983d0949aed78a16fa4782237dea7-Abstract-Conference.html). *NeurIPS 2022*.
+[^31]: Kim, T., Kim, J., Tae, Y., Park, C., Choi, J. H., & Choo, J. (2022). [Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift](https://openreview.net/forum?id=cGDAkQo1C0p). *ICLR 2022*.
 
 
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