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Update docs (#538)
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* docs: add FITS into the algo table;

* docs: update the reference of TEFN;

* docs: update pytorch intersphinx mapping link;

* docs: update docs for new added models CSAI and SegRNN;
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@WenjieDu
WenjieDu authored Oct 25, 2024
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10 changes: 7 additions & 3 deletions README.md
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Expand Up @@ -126,7 +126,6 @@ The paper references and links are all listed at the bottom of this file.
| Neural Net | iTransformer🧑‍🔧[^24] | ✅ | | | | | `2024 - ICLR` |
| Neural Net | ModernTCN[^38] | ✅ | | | | | `2024 - ICLR` |
| Neural Net | ImputeFormer🧑‍🔧[^34] | ✅ | | | | | `2024 - KDD` |
| Neural Net | SegRNN[^42] | ✅ | | | | | `2023 - arXiv` |
| Neural Net | SAITS[^1] | ✅ | | | | | `2023 - ESWA` |
| Neural Net | FreTS🧑‍🔧[^23] | ✅ | | | | | `2023 - NeurIPS` |
| Neural Net | Koopa🧑‍🔧[^29] | ✅ | | | | | `2023 - NeurIPS` |
Expand All @@ -137,6 +136,8 @@ The paper references and links are all listed at the bottom of this file.
| Neural Net | MICN🧑‍🔧[^27] | ✅ | | | | | `2023 - ICLR` |
| Neural Net | DLinear🧑‍🔧[^17] | ✅ | | | | | `2023 - AAAI` |
| Neural Net | TiDE🧑‍🔧[^28] | ✅ | | | | | `2023 - TMLR` |
| Neural Net | CSAI[^42] | ✅ | | | | | `2023 - arXiv` |
| Neural Net | SegRNN🧑‍🔧[^43] | ✅ | | | | | `2023 - arXiv` |
| Neural Net | SCINet🧑‍🔧[^30] | ✅ | | | | | `2022 - NeurIPS` |
| Neural Net | Nonstationary Tr.🧑‍🔧[^25] | ✅ | | | | | `2022 - NeurIPS` |
| Neural Net | FiLM🧑‍🔧[^22] | ✅ | | | | | `2022 - NeurIPS` |
Expand Down Expand Up @@ -510,6 +511,9 @@ Time-Series.AI</a>
[^41]: Xu, Z., Zeng, A., & Xu, Q. (2024).
[FITS: Modeling Time Series with 10k parameters](https://openreview.net/forum?id=bWcnvZ3qMb).
*ICLR 2024*.
[^42]: Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023).
[Segrnn: Segment recurrent neural network for long-term time series forecasting](https://github.com/lss-1138/SegRNN)
[^42]: Qian, L., Ibrahim, Z., Ellis, H. L., Zhang, A., Zhang, Y., Wang, T., & Dobson, R. (2023).
[Knowledge Enhanced Conditional Imputation for Healthcare Time-series](https://arxiv.org/abs/2312.16713).
*arXiv 2023*.
[^43]: Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023).
[SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting](https://arxiv.org/abs/2308.11200).
*arXiv 2023*.
8 changes: 8 additions & 0 deletions README_zh.md
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Expand Up @@ -121,6 +121,8 @@ PyPOTS当前支持多变量POTS数据的插补, 预测, 分类, 聚类以及异
| Neural Net | MICN🧑‍🔧[^27] | ✅ | | | | | `2023 - ICLR` |
| Neural Net | DLinear🧑‍🔧[^17] | ✅ | | | | | `2023 - AAAI` |
| Neural Net | TiDE🧑‍🔧[^28] | ✅ | | | | | `2023 - TMLR` |
| Neural Net | CSAI[^42] | ✅ | | | | | `2023 - arXiv` |
| Neural Net | SegRNN🧑‍🔧[^43] | ✅ | | | | | `2023 - arXiv` |
| Neural Net | SCINet🧑‍🔧[^30] | ✅ | | | | | `2022 - NeurIPS` |
| Neural Net | Nonstationary Tr.🧑‍🔧[^25] | ✅ | | | | | `2022 - NeurIPS` |
| Neural Net | FiLM🧑‍🔧[^22] | ✅ | | | | | `2022 - NeurIPS` |
Expand Down Expand Up @@ -482,3 +484,9 @@ Time-Series.AI</a>
[^41]: Xu, Z., Zeng, A., & Xu, Q. (2024).
[FITS: Modeling Time Series with 10k parameters](https://openreview.net/forum?id=bWcnvZ3qMb).
*ICLR 2024*.
[^42]: Qian, L., Ibrahim, Z., Ellis, H. L., Zhang, A., Zhang, Y., Wang, T., & Dobson, R. (2023).
[Knowledge Enhanced Conditional Imputation for Healthcare Time-series](https://arxiv.org/abs/2312.16713).
*arXiv 2023*.
[^43]: Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023).
[SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting](https://arxiv.org/abs/2308.11200).
*arXiv 2023*.
4 changes: 4 additions & 0 deletions docs/index.rst
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Expand Up @@ -165,6 +165,10 @@ The paper references are all listed at the bottom of this readme file.
+----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
| Neural Net | TiDE🧑‍🔧 :cite:`das2023tide` | ✅ | | | | | ``2023 - TMLR`` |
+----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
| Neural Net | CSAI :cite:`qian2023csai` | ✅ | | | | | ``2023 - arXiv`` |
+----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
| Neural Net | SegRNN🧑‍🔧 :cite:`lin2023segrnn` | ✅ | | | | | ``2023 - arXiv`` |
+----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
| Neural Net | SCINet🧑‍🔧 :cite:`liu2022scinet` | ✅ | | | | | ``2022 - NeurIPS`` |
+----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
| Neural Net | Nonstationary Tr🧑‍🔧 :cite:`liu2022nonstationary` | ✅ | | | | | ``2022 - NeurIPS`` |
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18 changes: 18 additions & 0 deletions docs/pypots.imputation.rst
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Expand Up @@ -28,6 +28,24 @@ pypots.imputation.tefn
:show-inheritance:
:inherited-members:

pypots.imputation.csai
------------------------------------

.. automodule:: pypots.imputation.csai
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

pypots.imputation.segrnn
------------------------------------

.. automodule:: pypots.imputation.segrnn
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

pypots.imputation.fits
------------------------------------

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6 changes: 6 additions & 0 deletions docs/references.bib
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Expand Up @@ -779,3 +779,9 @@ @inproceedings{jin2024timellm
url={https://openreview.net/forum?id=Unb5CVPtae}
}

@article{qian2023csai,
title={Knowledge Enhanced Conditional Imputation for Healthcare Time-series},
author={Qian, Linglong and Ibrahim, Zina and Ellis, Hugh Logan and Zhang, Ao and Zhang, Yuezhou and Wang, Tao and Dobson, Richard},
journal={arXiv preprint arXiv:2312.16713},
year={2023}
}
38 changes: 29 additions & 9 deletions pypots/imputation/csai/model.py
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Expand Up @@ -21,7 +21,8 @@


class CSAI(BaseNNImputer):
"""
"""The PyTorch implementation of the CSAI model :cite:`qian2023csai`.
Parameters
----------
n_steps :
Expand Down Expand Up @@ -58,29 +59,48 @@ class CSAI(BaseNNImputer):
The number of epochs for training the model.
patience :
The patience for the early-stopping mechanism. Given a positive integer, training will stop when no improvement is observed after the specified number of epochs. If set to None, early-stopping is disabled.
The patience for the early-stopping mechanism. Given a positive integer, the training process will be
stopped when the model does not perform better after that number of epochs.
Leaving it default as None will disable the early-stopping.
optimizer :
The optimizer used for model training. Defaults to the Adam optimizer if not specified.
The optimizer for model training.
If not given, will use a default Adam optimizer.
num_workers :
The number of subprocesses used for data loading. Setting this to `0` means that data loading is performed in the main process without using subprocesses.
The number of subprocesses to use for data loading.
`0` means data loading will be in the main process, i.e. there won't be subprocesses.
device :
The device for the model to run on, which can be a string, a :class:`torch.device` object, or a list of devices. If not provided, the model will attempt to use available CUDA devices first, then default to CPUs.
The device for the model to run on. It can be a string, a :class:`torch.device` object, or a list of them.
If not given, will try to use CUDA devices first (will use the default CUDA device if there are multiple),
then CPUs, considering CUDA and CPU are so far the main devices for people to train ML models.
If given a list of devices, e.g. ['cuda:0', 'cuda:1'], or [torch.device('cuda:0'), torch.device('cuda:1')] , the
model will be parallely trained on the multiple devices (so far only support parallel training on CUDA devices).
Other devices like Google TPU and Apple Silicon accelerator MPS may be added in the future.
saving_path :
The path for saving model checkpoints and tensorboard files during training. If not provided, models will not be saved automatically.
The path for automatically saving model checkpoints and tensorboard files (i.e. loss values recorded during
training into a tensorboard file). Will not save if not given.
model_saving_strategy :
The strategy for saving model checkpoints. Can be one of [None, "best", "better", "all"]. "best" saves the best model after training, "better" saves any model that improves during training, and "all" saves models after each epoch. If set to None, no models will be saved.
The strategy to save model checkpoints. It has to be one of [None, "best", "better", "all"].
No model will be saved when it is set as None.
The "best" strategy will only automatically save the best model after the training finished.
The "better" strategy will automatically save the model during training whenever the model performs
better than in previous epochs.
The "all" strategy will save every model after each epoch training.
verbose :
Whether to print training logs during the training process.
Whether to print out the training logs during the training process.
Notes
-----
CSAI (Consistent Sequential Imputation) is a bidirectional model designed for time-series imputation. It employs a forward and backward GRU network to handle missing data, using consistency and reconstruction losses to improve accuracy. The model supports various training configurations, such as interval computations, early-stopping, and multiple devices for training. Results can be saved based on the specified saving strategy, and tensorboard files are generated for tracking the model's performance over time.
CSAI (Consistent Sequential Imputation) is a bidirectional model designed for time-series imputation.
It employs a forward and backward GRU network to handle missing data, using consistency and reconstruction losses
to improve accuracy. The model supports various training configurations, such as interval computations,
early-stopping, and multiple devices for training. Results can be saved based on the specified saving strategy,
and tensorboard files are generated for tracking the model's performance over time.
"""

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