Update docs

README.md

@@ -113,47 +113,47 @@ 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** |
-|:--------------|:----------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:-------------------|
-| LLM | Gungnir 🚀 [^36]  | ✅ | ✅ | ✅ | ✅ | ✅ | `Later in 2024` |
-| Neural Net | ImputeFormer🧑‍🔧[^34] | ✅ | | | | | `2024 - KDD` |
-| 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 | StemGNN🧑‍🔧[^33] | ✅ | | | | | `2020 - NeurIPS` |
-| Neural Net | Reformer🧑‍🔧[^32] | ✅ | | | | | `2020 - ICLR` |
-| 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 | TCN🧑‍🔧[^35] | ✅ | | | | | `2018 - arXiv` |
-| Neural Net | Transformer🧑‍🔧[^2] | ✅ | | | | | `2017 - NeurIPS` |
-| Naive | Lerp | ✅ | | | | | |
-| Naive | LOCF/NOCB | ✅ | | | | | |
-| Naive | Mean | ✅ | | | | | |
-| Naive | Median | ✅ | | | | | |
+| **Type** | **Algo**  | **IMPU** | **FORE** | **CLAS** | **CLUS** | **ANOD** | **Year - Venue** |
+|:--------------|:---------------------------------------------------------------------------------------------------------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:-------------------|
+| LLM | <a href="https://time-series.ai"><img src="https://time-series.ai/static/figs/robot.svg" width="26px"> Time-Series.AI</a> [^36] | ✅ | ✅ | ✅ | ✅ | ✅ | `Later in 2024` |
+| Neural Net | ImputeFormer🧑‍🔧[^34]  | ✅ | | | | | `2024 - KDD` |
+| 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 | StemGNN🧑‍🔧[^33]  | ✅ | | | | | `2020 - NeurIPS` |
+| Neural Net | Reformer🧑‍🔧[^32]  | ✅ | | | | | `2020 - ICLR` |
+| 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 | TCN🧑‍🔧[^35]  | ✅ | | | | | `2018 - arXiv` |
+| Neural Net | Transformer🧑‍🔧[^2]  | ✅ | | | | | `2017 - NeurIPS` |
+| Naive | Lerp  | ✅ | | | | | |
+| Naive | LOCF/NOCB  | ✅ | | | | | |
+| Naive | Mean  | ✅ | | | | | |
+| Naive | Median  | ✅ | | | | | |
 
 💯 Contribute your model right now to increase your research impact! PyPOTS downloads are increasing rapidly (**[300K+ in total and 1K+ daily on PyPI so far](https://www.pepy.tech/projects/pypots)**),
 and your work will be widely used and cited by the community.
@@ -394,3 +394,4 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
 [^35]: Bai, S., Kolter, J. Z., & Koltun, V. (2018). [An empirical evaluation of generic convolutional and recurrent networks for sequence modeling](https://arxiv.org/abs/1803.01271). *arXiv 2018*.
 [^36]: Project Gungnir, the world 1st LLM for time-series multitask modeling, will meet you soon. 🚀 Missing values and variable lengths in your datasets?
  Hard to perform multitask learning with your time series? Not problems no longer. We'll open application for public beta test recently ;-) Follow us, and stay tuned!
+ <a href="https://time-series.ai"><img src="https://time-series.ai/static/figs/robot.svg" width="20px" align="center"> Time-Series.AI</a>