From 712708100ab8793b167739629c949396db6a46e8 Mon Sep 17 00:00:00 2001 From: Wenjie Du Date: Mon, 13 May 2024 15:19:44 +0800 Subject: [PATCH] Update docs and references (#410) * docs: rename issue label "new model" to "new algo"; * docs: update docs and references; --- .github/ISSUE_TEMPLATE/model-addition.yml | 2 +- README.md | 83 +++++++------ README_zh.md | 72 +++++++----- docs/index.rst | 22 +++- docs/pypots.imputation.rst | 66 ++++++++++- docs/references.bib | 137 +++++++++++++++++++--- pypots/imputation/revinscinet/model.py | 2 +- pypots/imputation/scinet/model.py | 2 +- 8 files changed, 292 insertions(+), 94 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/model-addition.yml b/.github/ISSUE_TEMPLATE/model-addition.yml index 6024f67a..c4ee5842 100644 --- 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 index 802fb2db..db55334b 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,7 @@ arXiv DOI - README in Chinese + README in Chinese

@@ -86,7 +86,7 @@ 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. @@ -94,10 +94,10 @@ This functionality is implemented with the [Microsoft NNI](https://github.com/mi 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
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*.
diff --git a/README_zh.md b/README_zh.md index 59159a3f..98ecc5e8 100644 --- a/README_zh.md +++ b/README_zh.md @@ -53,7 +53,7 @@ arXiv DOI - README in English + README in English

@@ -83,46 +83,51 @@ ## ❖ 支持的算法 PyPOTS当前支持多变量POTS数据的插补，预测，分类，聚类以及异常检测五类任务。下表描述了当前PyPOTS中所集成的算法以及对应不同任务的可用性。 -符号 ✅ 表示该算法当前可用于相应的任务（注意，目前模型尚不支持的任务在未来版本中可能会逐步添加，敬请关注！)。 +符号`✅`表示该算法当前可用于相应的任务（注意，目前模型尚不支持的任务在未来版本中可能会逐步添加，敬请关注！)。 算法的参考文献以及论文链接在该文档底部可以找到。 🌟 自**v0.2**版本开始, PyPOTS中所有神经网络模型都支持超参数调优。该功能基于[微软的NNI](https://github.com/microsoft/nni)框架实现。 你可以通过参考我们的时间序列插补综述项目的代码[Awesome_Imputation](https://github.com/WenjieDu/Awesome_Imputation)来了解如何使用PyPOTS调优模型的超参。 -🔥 请注意: Transformer, iTransformer, FreTS, Crossformer, PatchTST, DLinear, ETSformer, Pyraformer, Nonstationary Transformer, FiLM, FEDformer, Informer, Autoformer -模型在它们的原始论文中并未用作插补方法，因此这些模型的输入中不能带有缺失值， 所以无法接受POTS数据作为输入。 +🔥 请注意: 表格中名称带有`🧑‍🔧`的模型（例如Transformer, iTransformer, Informer等）在它们的原始论文中并非作为可以处理POTS数据的算法提出， +所以这些模型的输入中不能带有缺失值，无法接受POTS数据作为输入，更加不是插补算法。 **为了使上述模型能够适用于POTS数据，我们采用了与[SAITS论文](https://arxiv.org/pdf/2202.08516)中相同的embedding策略和训练方法（ORT+MIT）对它们进行改进**。 | **类型** | **算法** | **插补** | **预测** | **分类** | **聚类** | **异常检测** | **年份 - 刊物** | |:--------------|:-----------------------------------|:------:|:------:|:------:|:------:|:--------:|:-----------------| -| 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
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 | ✅ | | | | | | +| 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生态系统 @@ -331,6 +336,9 @@ PyPOTS社区是一个开放、透明、友好的社区，让我们共同努力 [^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*.
diff --git a/docs/index.rst b/docs/index.rst index 57c0d8dc..c2b5101c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -72,7 +72,7 @@ Welcome to PyPOTS docs! :alt: arXiv DOI :target: https://arxiv.org/abs/2305.18811 -.. image:: https://img.shields.io/badge/README-%F0%9F%87%A8%F0%9F%87%B3中文版-FCEFE8 +.. image:: https://pypots.com/figs/pypots_logos/readme/CN.svg :alt: README in Chinese :target: https://github.com/WenjieDu/PyPOTS/blob/main/README_zh.md @@ -117,9 +117,9 @@ This functionality is implemented with the `Microsoft NNI `_ 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) +🔥 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 `_. The task types are abbreviated as follows: **IMPU**: Imputation; **FORE**: Forecasting; @@ -135,6 +135,8 @@ The paper references are all listed at the bottom of this readme file. +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ | Neural Net | FreTS :cite:`yi2023frets` | ✅ | | | | | ``2023 - NeurIPS`` | +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ +| Neural Net | Koopa :cite:`liu2023koopa` | ✅ | | | | | ``2023 - NeurIPS`` | ++----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ | Neural Net | Crossformer :cite:`nie2023patchtst` | ✅ | | | | | ``2023 - ICLR`` | +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ | Neural Net | TimesNet :cite:`wu2023timesnet` | ✅ | | | | | ``2023 - ICLR`` | @@ -143,11 +145,19 @@ The paper references are all listed at the bottom of this readme file. +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ | Neural Net | ETSformer :cite:`woo2023etsformer` | ✅ | | | | | ``2023 - ICLR`` | +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ +| Neural Net | MICN :cite:`wang2023micn` | ✅ | | | | | ``2023 - ICLR`` | ++----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ | Neural Net | DLinear :cite:`zeng2023dlinear` | ✅ | | | | | ``2023 - AAAI`` | +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ -| Neural Net | Nonstationary Tran. :cite:`liu2022nonstationary` | ✅ | | | | | ``2023 - NeurIPS`` | +| Neural Net | TiDE :cite:`das2023tide` | ✅ | | | | | ``2023 - TMLR`` | ++----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ +| Neural Net | SCINet :cite:`liu2022scinet` | ✅ | | | | | ``2022 - NeurIPS`` | ++----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ +| Neural Net | Nonstationary Tran. :cite:`liu2022nonstationary` | ✅ | | | | | ``2022 - NeurIPS`` | ++----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ +| Neural Net | FiLM :cite:`zhou2022film` | ✅ | | | | | ``2022 - NeurIPS`` | +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ -| Neural Net | FiLM :cite:`zhou2022film` | ✅ | | | | | ``2023 - NeurIPS`` | +| Neural Net | RevIN_SCInet :cite:`kim2022revin` | ✅ | | | | | ``2022 - ICLR`` | +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ | Neural Net | Pyraformer :cite:`liu2022pyraformer` | ✅ | | | | | ``2022 - ICLR`` | +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+ diff --git a/docs/pypots.imputation.rst b/docs/pypots.imputation.rst index 37b25519..3b0b935e 100644 --- a/docs/pypots.imputation.rst +++ b/docs/pypots.imputation.rst @@ -28,6 +28,15 @@ pypots.imputation.itransformer :show-inheritance: :inherited-members: +pypots.imputation.koopa +------------------------------------ + +.. automodule:: pypots.imputation.koopa + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + pypots.imputation.frets ------------------------------ @@ -64,6 +73,23 @@ pypots.imputation.patchtst :show-inheritance: :inherited-members: +pypots.imputation.etsformer +------------------------------ + +.. automodule:: pypots.imputation.etsformer + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + +pypots.imputation.micn +------------------------------ +.. automodule:: pypots.imputation.micn + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + pypots.imputation.dlinear ------------------------------ @@ -73,10 +99,28 @@ pypots.imputation.dlinear :show-inheritance: :inherited-members: -pypots.imputation.etsformer +pypots.imputation.tide ------------------------------ -.. automodule:: pypots.imputation.etsformer +.. automodule:: pypots.imputation.tide + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + +pypots.imputation.scinet +------------------------------ + +.. automodule:: pypots.imputation.scinet + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + +pypots.imputation.nonstationary_transformer +------------------------------ + +.. automodule:: pypots.imputation.nonstationary_transformer :members: :undoc-members: :show-inheritance: @@ -91,6 +135,24 @@ pypots.imputation.film :show-inheritance: :inherited-members: +pypots.imputation.revin_scinet +------------------------------ + +.. automodule:: pypots.imputation.film + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + +pypots.imputation.pyraformer +------------------------------ + +.. automodule:: pypots.imputation.pyraformer + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + pypots.imputation.fedformer ------------------------------ diff --git a/docs/references.bib b/docs/references.bib index 2c07158a..def627d1 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -355,20 +355,6 @@ @article{wu2015TimeSeries url = {https://eudl.eu/doi/10.4108/icst.iniscom.2015.258269} } -@article{yoon2017EstimatingMissing, -title = {Estimating {{Missing Data}} in {{Temporal Data Streams Using Multi-directional Recurrent Neural Networks}}}, -author = {Yoon, Jinsung and Zame, William R. and {van der Schaar}, Mihaela}, -year = {2017}, -month = nov, -journal = {arXiv:1711.08742 [cs]}, -eprint = {1711.08742}, -eprinttype = {arxiv}, -primaryclass = {cs}, -url = {http://arxiv.org/abs/1711.08742}, -archiveprefix = {arXiv}, -keywords = {Computer Science - Machine Learning} -} - @article{yuan2019E2GAN, title = {{{E}}{$^{2}$}{{GAN}}: {{End-to-End Generative Adversarial Network}} for {{Multivariate Time Series Imputation}}}, author = {Yuan, Xiaojie and Luo, Yonghong and Zhang, Ying and Cai, Xiangrui}, @@ -616,3 +602,126 @@ @article{das2023tide year={2023}, url={https://openreview.net/forum?id=pCbC3aQB5W}, } + +@inproceedings{chen2023contiformer, +title={ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling}, +author={Yuqi Chen and Kan Ren and Yansen Wang and Yuchen Fang and Weiwei Sun and Dongsheng Li}, +booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, +year={2023}, +url={https://openreview.net/forum?id=YJDz4F2AZu} +} + +@inproceedings{lee2024pits, +title={Learning to Embed Time Series Patches Independently}, +author={Seunghan Lee and Taeyoung Park and Kibok Lee}, +booktitle={The Twelfth International Conference on Learning Representations}, +year={2024}, +url={https://openreview.net/forum?id=WS7GuBDFa2} +} + +@inproceedings{wang2023micn, +title={{MICN}: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting}, +author={Huiqiang Wang and Jian Peng and Feihu Huang and Jince Wang and Junhui Chen and Yifei Xiao}, +booktitle={The Eleventh International Conference on Learning Representations}, +year={2023}, +url={https://openreview.net/forum?id=zt53IDUR1U} +} + +@inproceedings{wang2024timemixer, +title={TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting}, +author={Shiyu Wang and Haixu Wu and Xiaoming Shi and Tengge Hu and Huakun Luo and Lintao Ma and James Y. Zhang and JUN ZHOU}, +booktitle={The Twelfth International Conference on Learning Representations}, +year={2024}, +url={https://openreview.net/forum?id=7oLshfEIC2} +} + +@article{gu2023mamba, +title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces}, +author={Gu, Albert and Dao, Tri}, +journal={arXiv preprint arXiv:2312.00752}, +year={2023} +} + +@article{zhang2022lightts, +title={Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures}, +author={Tianping Zhang and Yizhuo Zhang and Wei Cao and Jiang Bian and Xiaohan Yi and Shun Zheng and Jian Li}, +year={2022}, +eprint={2207.01186}, +archivePrefix={arXiv}, +primaryClass={cs.LG} +} + +@article{lin2023segrnn, +title={{SegRNN}: Segment Recurrent Neural Network for Long-Term Time Series Forecasting}, +author={Shengsheng Lin and Weiwei Lin and Wentai Wu and Feiyu Zhao and Ruichao Mo and Haotong Zhang}, +year={2023}, +eprint={2308.11200}, +archivePrefix={arXiv}, +primaryClass={cs.LG} +} + +@article{chen2023tsmixer, +title={{TSMixer}: An All-MLP Architecture for Time Series Forecasting}, +author={Si-An Chen and Chun-Liang Li and Nate Yoder and Sercan O. Arik and Tomas Pfister}, +year={2023}, +eprint={2303.06053}, +archivePrefix={arXiv}, +primaryClass={cs.LG} +} + +@inproceedings{choi2024timecib, +title={Conditional Information Bottleneck Approach for Time Series Imputation}, +author={MinGyu Choi and Changhee Lee}, +booktitle={The Twelfth International Conference on Learning Representations}, +year={2024}, +url={https://openreview.net/forum?id=K1mcPiDdOJ} +} + +@article{gao2024units, +title={{UniTS}: Building a Unified Time Series Model}, +author={Gao, Shanghua and Koker, Teddy and Queen, Owen and Hartvigsen, Thomas and Tsiligkaridis, Theodoros and Zitnik, Marinka}, +journal={arXiv}, +url={https://arxiv.org/pdf/2403.00131.pdf}, +year={2024} +} + +@article{liu2024timesurl, +title={{TimesURL}: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning}, +author={Liu, Jiexi and Chen, Songcan}, +volume={38}, +url={https://ojs.aaai.org/index.php/AAAI/article/view/29299}, +DOI={10.1609/aaai.v38i12.29299}, +number={12}, +journal={Proceedings of the AAAI Conference on Artificial Intelligence}, +year={2024}, +month={Mar.}, +pages={13918-13926}, +} + +@inproceedings{luo2024moderntcn, +title={Modern{TCN}: A Modern Pure Convolution Structure for General Time Series Analysis}, +author={Luo Donghao, Wue Xue}, +booktitle={The Twelfth International Conference on Learning Representations}, +year={2024}, +url={https://openreview.net/forum?id=vpJMJerXHU} +} + +@inproceedings{liu2022scinet, +author = {LIU, Minhao and Zeng, Ailing and Chen, Muxi and Xu, Zhijian and LAI, Qiuxia and Ma, Lingna and Xu, Qiang}, +booktitle = {Advances in Neural Information Processing Systems}, +editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, +pages = {5816--5828}, +publisher = {Curran Associates, Inc.}, +title = {SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction}, +url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/266983d0949aed78a16fa4782237dea7-Paper-Conference.pdf}, +volume = {35}, +year = {2022} +} + +@inproceedings{kim2022revin, +title={Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift}, +author={Taesung Kim and Jinhee Kim and Yunwon Tae and Cheonbok Park and Jang-Ho Choi and Jaegul Choo}, +booktitle={International Conference on Learning Representations}, +year={2022}, +url={https://openreview.net/forum?id=cGDAkQo1C0p} +} diff --git a/pypots/imputation/revinscinet/model.py b/pypots/imputation/revinscinet/model.py index e5f03e6d..cd63982e 100644 --- a/pypots/imputation/revinscinet/model.py +++ b/pypots/imputation/revinscinet/model.py @@ -23,7 +23,7 @@ class RevIN_SCINet(BaseNNImputer): """The PyTorch implementation of the RevIN_SCINet model. - RevIN_SCINet is originally proposed by et al. in :cite:`wu2021autoformer`. + RevIN_SCINet is originally proposed by Kim et al. in :cite:`kim2022revin`. Parameters ---------- diff --git a/pypots/imputation/scinet/model.py b/pypots/imputation/scinet/model.py index 884bda45..667e05f8 100644 --- a/pypots/imputation/scinet/model.py +++ b/pypots/imputation/scinet/model.py @@ -23,7 +23,7 @@ class SCINet(BaseNNImputer): """The PyTorch implementation of the SCINet model. - SCINet is originally proposed by Wu et al. in :cite:`wu2021autoformer`. + SCINet is originally proposed by Liu et al. in :cite:`liu2022scinet`. Parameters ----------