Skip to content

Update docs and references #410

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 12 commits into from
May 13, 2024
Merged

Update docs and references #410

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
12 commits
Select commit Hold shift + click to select a range
ef1a407
docs: rename issue label "new model" to "new algo";
WenjieDu May 9, 2024
2246a4f
docs: update README and references;
WenjieDu May 9, 2024
1d6646b
docs: update references;
WenjieDu May 9, 2024
99dffee
docs: update references;
WenjieDu May 10, 2024
d4853aa
docs: update README;
WenjieDu May 11, 2024
30d086c
docs: update references;
WenjieDu May 11, 2024
3d182f7
docs: update references;
WenjieDu May 11, 2024
400da53
docs: update README;
WenjieDu May 11, 2024
3df296f
Merge branch 'dev' into (docs)update_docs
WenjieDu May 12, 2024
62f02c9
docs: update docs;
WenjieDu May 12, 2024
4ab1e6d
docs: update README;
WenjieDu May 12, 2024
6bb3463
docs: update README;
WenjieDu May 12, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion .github/ISSUE_TEMPLATE/model-addition.yml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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
Expand Down
83 changes: 46 additions & 37 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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>

Expand Down Expand Up @@ -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;
Expand All @@ -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
Expand Down Expand Up @@ -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*.


<details>
Expand Down
Loading
Loading