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WenjieDu committed Nov 5, 2023
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20 changes: 12 additions & 8 deletions README.md
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Expand Up @@ -107,7 +107,7 @@ With PyGrinder, you can introduce synthetic missing values into your datasets wi
</a>

👈 Now we have the beans, the grinder, and the pot, how to brew us a cup of coffee? Tutorials are necessary!
Considering the future workload, PyPOTS tutorials is released in a single repo,
Considering the future workload, PyPOTS tutorials are released in a single repo,
and you can find them in [BrewPOTS](https://github.com/WenjieDu/BrewPOTS).
Take a look at it now, and learn how to brew your POTS datasets!

Expand All @@ -133,6 +133,7 @@ conda update -c conda-forge pypots # update pypots to the latest version
Alternatively, you can install from the latest source code with the latest features but may be not officially released yet:
> pip install https://github.com/WenjieDu/PyPOTS/archive/main.zip


## ❖ Usage
Besides [BrewPOTS](https://github.com/WenjieDu/BrewPOTS), you can also find a simple and quick-start tutorial notebook
on Google Colab with [this link](https://colab.research.google.com/drive/1HEFjylEy05-r47jRy0H9jiS_WhD0UWmQ?usp=sharing).
Expand Down Expand Up @@ -164,7 +165,8 @@ dataset = {"X": X}
print(dataset["X"].shape) # (11988, 48, 37), 11988 samples, 48 time steps, 37 features
# Model training. This is PyPOTS showtime.
saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_inner=128, n_heads=4, d_k=64, d_v=64, dropout=0.1, epochs=10)
saits.fit(dataset) # train the model. Here I use the whole dataset as the training set, because ground truth is not visible to the model.
# Here I use the whole dataset as the training set because ground truth is not visible to the model, you can also split it into train/val/test sets
saits.fit(dataset)
imputation = saits.impute(dataset) # impute the originally-missing values and artificially-missing values
mae = cal_mae(imputation, X_intact, indicating_mask) # calculate mean absolute error on the ground truth (artificially-missing values)
```
Expand All @@ -176,12 +178,12 @@ PyPOTS supports imputation, classification, clustering, and forecasting tasks on

| ***`Imputation`*** | 🚥 | 🚥 | 🚥 |
|:----------------------:|:-----------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
| **Type** | **Abbr.** | **Full name of the algorithm/model/paper** | **Year** |
| **Type** | **Abbr.** | **Full name of the algorithm/model** | **Year** |
| Neural Net | SAITS | Self-Attention-based Imputation for Time Series [^1] | 2023 |
| Neural Net | Transformer | Attention is All you Need [^2];<br>Self-Attention-based Imputation for Time Series [^1];<br><sub>Note: proposed in [^2], and re-implemented as an imputation model in [^1].</sub> | 2017 |
| Neural Net | CSDI | Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [^12] | 2021 |
| Neural Net | US-GAN | Generative Semi-supervised Learning for Multivariate Time Series Imputation [^10] | 2021 |
| Neural Net | GP-VAE | GP-VAE: Deep Probabilistic Time Series Imputation [^11] | 2020 |
| Neural Net | US-GAN | Unsupervised GAN for Multivariate Time Series Imputation [^10] | 2021 |
| Neural Net | GP-VAE | Gaussian Process Variational Autoencoder [^11] | 2020 |
| Neural Net | BRITS | Bidirectional Recurrent Imputation for Time Series [^3] | 2018 |
| Neural Net | M-RNN | Multi-directional Recurrent Neural Network [^9] | 2019 |
| Naive | LOCF | Last Observation Carried Forward | - |
Expand Down Expand Up @@ -270,16 +272,17 @@ Your star is your recognition to PyPOTS, and it matters!
</i></b>
</summary>
<a href="https://github.com/WenjieDu/PyPOTS/stargazers">
<img alt="PyPOTS stargazers" src="https://reporoster.com/stars/dark/WenjieDu/PyPOTS">
<img alt="PyPOTS stargazers" src="http://reporoster.com/stars/dark/WenjieDu/PyPOTS">
</a>
<br>
<a href="https://github.com/WenjieDu/PyPOTS/network/members">
<img alt="PyPOTS forkers" src="https://reporoster.com/forks/dark/WenjieDu/PyPOTS">
<img alt="PyPOTS forkers" src="http://reporoster.com/forks/dark/WenjieDu/PyPOTS">
</a>
</details>
👀 Check out a full list of our users' affiliations [on PyPOTS website here](https://pypots.com/users/)!
## ❖ Community
We care about the feedback from our users, so we're building PyPOTS community on
Expand All @@ -291,6 +294,7 @@ We care about the feedback from our users, so we're building PyPOTS community on
If you have any suggestions or want to contribute ideas or share time-series related papers, join us and tell.
PyPOTS community is open, transparent, and surely friendly. Let's work together to build and improve PyPOTS!
[//]: # (Use APA reference style below)
[^1]: Du, W., Cote, D., & Liu, Y. (2023). [SAITS: Self-Attention-based Imputation for Time Series](https://doi.org/10.1016/j.eswa.2023.119619). *Expert systems with applications*.
[^2]: Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). [Attention is All you Need](https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html). *NeurIPS 2017*.
Expand All @@ -304,7 +308,7 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
[^10]: Miao, X., Wu, Y., Wang, J., Gao, Y., Mao, X., & Yin, J. (2021). [Generative Semi-supervised Learning for Multivariate Time Series Imputation](https://ojs.aaai.org/index.php/AAAI/article/view/17086). *AAAI 2021*.
[^11]: Fortuin, V., Baranchuk, D., Raetsch, G. & Mandt, S. (2020). [GP-VAE: Deep Probabilistic Time Series Imputation](https://proceedings.mlr.press/v108/fortuin20a.html). *AISTATS 2020*.
[^12]: Tashiro, Y., Song, J., Song, Y., & Ermon, S. (2021). [CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation](https://proceedings.neurips.cc/paper/2021/hash/cfe8504bda37b575c70ee1a8276f3486-Abstract.html). *NeurIPS 2021*.
[^13]: Rubin, D. B. (1976). [Inference and missing data](https://academic.oup.com/biomet/article-abstract/63/3/581/270932). *Biometrika*, 63(3), 581-592.
[^13]: Rubin, D. B. (1976). [Inference and missing data](https://academic.oup.com/biomet/article-abstract/63/3/581/270932). *Biometrika*.
<details>
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Expand Up @@ -104,7 +104,7 @@ As you can see, there is a coffee pot in the PyPOTS logo.
:target: https://github.com/WenjieDu/TSDB

👈 To make various open-source time-series datasets readily available to our users,
PyPOTS gets supported by its ecosystem library <i>Time Series Data Beans (TSDB)</i>, a toolbox making loading time-series datasets super easy!
PyPOTS gets supported by its ecosystem library *Time Series Data Beans (TSDB)*, a toolbox making loading time-series datasets super easy!
Visit `TSDB <https://github.com/WenjieDu/TSDB>`_ right now to know more about this handy tool 🛠, and it now supports a total of 168 open-source datasets!

.. image:: https://pypots.com/figs/pypots_logos/PyGrinder_logo_FFBG.svg
Expand Down Expand Up @@ -154,22 +154,23 @@ Additionally, we present you a usage example of imputing missing values in time
^^^^^^^^^^^^^^^^^^^^^^^
PyPOTS supports imputation, classification, clustering, and forecasting tasks on multivariate time series with missing values. The currently available algorithms of four tasks are cataloged in the following table with four partitions. The paper references are all listed at the bottom of this readme file. Please refer to them if you want more details.

============================== ================ ========================================================================= ====== =========
Task Type Algorithm Year Reference
============================== ================ ========================================================================= ====== =========
Imputation Neural Network SAITS (Self-Attention-based Imputation for Time Series) 2022 :cite:`du2023SAITS`
Imputation Neural Network Transformer 2017 :cite:`vaswani2017Transformer`, :cite:`du2023SAITS`
Imputation Neural Network US-GAN 2021 :cite:`miao2021SSGAN`
Imputation Neural Network GP-VAE 2020 :cite:`fortuin2020GPVAEDeep`
Imputation, Classification Neural Network BRITS (Bidirectional Recurrent Imputation for Time Series) 2018 :cite:`cao2018BRITS`
Imputation Neural Network M-RNN (Multi-directional Recurrent Neural Network) 2019 :cite:`yoon2019MRNN`
Imputation Naive LOCF (Last Observation Carried Forward) / /
Classification Neural Network GRU-D 2018 :cite:`che2018GRUD`
Classification Neural Network Raindrop 2022 :cite:`zhang2022Raindrop`
Clustering Neural Network CRLI (Clustering Representation Learning on Incomplete time-series data) 2021 :cite:`ma2021CRLI`
Clustering Neural Network VaDER (Variational Deep Embedding with Recurrence) 2019 :cite:`dejong2019VaDER`
Forecasting Probabilistic BTTF (Bayesian Temporal Tensor Factorization) 2021 :cite:`chen2021BTMF`
============================== ================ ========================================================================= ====== =========
============================== ================ ======================================================================================== ====== =========
Task Type Algorithm Year Reference
============================== ================ ======================================================================================== ====== =========
Imputation Neural Net SAITS (Self-Attention-based Imputation for Time Series) 2022 :cite:`du2023SAITS`
Imputation Neural Net Transformer 2017 :cite:`vaswani2017Transformer`, :cite:`du2023SAITS`
Imputation Neural Net US-GAN (Unsupervised GAN for Multivariate Time Series Imputation) 2021 :cite:`miao2021SSGAN`
Imputation Neural Net CSDI (Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation) 2021 :cite:`tashiro2021csdi`
Imputation Neural Net GP-VAE (Gaussian Process Variational Autoencoder) 2020 :cite:`fortuin2020GPVAEDeep`
Imputation, Classification Neural Net BRITS (Bidirectional Recurrent Imputation for Time Series) 2018 :cite:`cao2018BRITS`
Imputation Neural Net M-RNN (Multi-directional Recurrent Neural Network) 2019 :cite:`yoon2019MRNN`
Imputation Naive LOCF (Last Observation Carried Forward) / /
Classification Neural Net GRU-D 2018 :cite:`che2018GRUD`
Classification Neural Net Raindrop 2022 :cite:`zhang2022Raindrop`
Clustering Neural Net CRLI (Clustering Representation Learning on Incomplete time-series data) 2021 :cite:`ma2021CRLI`
Clustering Neural Net VaDER (Variational Deep Embedding with Recurrence) 2019 :cite:`dejong2019VaDER`
Forecasting Probabilistic BTTF (Bayesian Temporal Tensor Factorization) 2021 :cite:`chen2021BTMF`
============================== ================ ======================================================================================== ====== =========


❖ Citing PyPOTS
Expand Down Expand Up @@ -276,7 +277,7 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
.. toctree::
:maxdepth: 4
:hidden:
:caption: Code Documentation
:caption: Docs of PyPOTS Ecosystem

model_api
pypots
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