pypots 0.0.11

A Python Toolbox for Data Mining on Partially-Observed Time Series

Welcome to PyPOTS

A Python Toolbox for Data Mining on Partially-Observed Time Series

⦿ Motivation: Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modeling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this blank.

⦿ Mission: PyPOTS is born to become a handy toolbox that is going to make data mining on POTS easy rather than tedious, to help engineers and researchers focus more on the core problems in their hands rather than on how to deal with the missing parts in their data. PyPOTS will keep integrating classical and the latest state-of-the-art data mining algorithms for partially-observed multivariate time series. For sure, besides various algorithms, PyPOTS is going to have unified APIs together with detailed documentation and interactive examples across algorithms as tutorials.

To make various open-source time-series datasets readily available to our users, PyPOTS gets supported by project TSDB (Time-Series DataBase), a toolbox making loading time-series datasets super easy!

Visit TSDB right now to know more about this handy tool 🛠! It now supports a total of 119 open-source datasets.

❖ Installation

PyPOTS now is available on ❗️

Install it with conda install pypots, you may need to specify the channel with option -c conda-forge

Install the latest release from PyPI:

pip install pypots

or install from the source code with the latest features not officially released in a version:

pip install https://github.com/WenjieDu/PyPOTS/archive/main.zip

Below is an example applying SAITS in PyPOTS to impute missing values in the dataset PhysioNet2012:

import numpy as np
from sklearn.preprocessing import StandardScaler
from pypots.data import load_specific_dataset, mcar, masked_fill
from pypots.imputation import SAITS
from pypots.utils.metrics import cal_mae
# Data preprocessing. Tedious, but PyPOTS can help. 🤓
data = load_specific_dataset('physionet_2012')  # PyPOTS will automatically download and extract it.
X = data['X']
num_samples = len(X['RecordID'].unique())
X = X.drop('RecordID', axis = 1)
X = StandardScaler().fit_transform(X.to_numpy())
X = X.reshape(num_samples, 48, -1)
X_intact, X, missing_mask, indicating_mask = mcar(X, 0.1) # hold out 10% observed values as ground truth
X = masked_fill(X, 1 - missing_mask, np.nan)
dataset = {"X": X}
# Model training. This is PyPOTS showtime. 💪
saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_inner=128, n_head=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.
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)

❖ Available Algorithms

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.

Imputation	🚥	🚥	🚥
Type	Abbr.	Full name of the algorithm/model/paper	Year
Neural Net	SAITS	Self-Attention-based Imputation for Time Series [^1]	2023
Neural Net	Transformer	Attention is All you Need [^2]; Self-Attention-based Imputation for Time Series [^1]; Note: proposed in [^2], and re-implemented as an imputation model in [^1].	2017
Neural Net	BRITS	Bidirectional Recurrent Imputation for Time Series [^3]	2018
Naive	LOCF	Last Observation Carried Forward	-
Classification	🚥	🚥	🚥
Type	Abbr.	Full name of the algorithm/model/paper	Year
Neural Net	BRITS	Bidirectional Recurrent Imputation for Time Series [^3]	2018
Neural Net	GRU-D	Recurrent Neural Networks for Multivariate Time Series with Missing Values [^4]	2018
Neural Net	Raindrop	Graph-Guided Network for Irregularly Sampled Multivariate Time Series [^5]	2022
Clustering	🚥	🚥	🚥
Type	Abbr.	Full name of the algorithm/model/paper	Year
Neural Net	CRLI	Clustering Representation Learning on Incomplete time-series data [^6]	2021
Neural Net	VaDER	Variational Deep Embedding with Recurrence [^7]	2019
Forecasting	🚥	🚥	🚥
Type	Abbr.	Full name of the algorithm/model/paper	Year
Probabilistic	BTTF	Bayesian Temporal Tensor Factorization [^8]	2021

❖ Citing PyPOTS

We are pursuing to publish a short paper introducing PyPOTS in prestigious academic venues, e.g. JMLR (track for Machine Learning Open Source Software). Before that, PyPOTS is using its DOI from Zenodo for reference. If you use PyPOTS in your research, please cite it as below and 🌟star this repository to make others notice this work. 🤗

@misc{du2022PyPOTS,
author = {Wenjie Du},
title = {{PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series}},
year = {2022},
howpublished = {\url{https://github.com/wenjiedu/pypots}},
url = {\url{https://github.com/wenjiedu/pypots}},
doi = {10.5281/zenodo.6823221},
}

or

Wenjie Du. (2022). PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series. Zenodo. https://doi.org/10.5281/zenodo.6823221

❖ Contribution

You're very welcome to contribute to this exciting project!

By committing your code, you'll

• be listed as one of PyPOTS contributors: ;
• get mentioned in our release notes;

Besides, you can also contribute to PyPOTS by simply staring🌟 this repo to help more people notice it. Your star is your recognition to PyPOTS, and it matters!

👏 Click here to view PyPOTS stargazers and forkers.
We're so proud to have more and more awesome users, as well as more bright ✨stars:

❖ Attention 👀

The documentation and tutorials are under construction.

‼️ PyPOTS is currently under developing. If you like it and look forward to its growth, please give PyPOTS a star and watch it to keep you posted on its progress and to let me know that its development is meaningful. If you have any feedback, or want to contribute ideas/suggestions or share time-series related algorithms/papers, please join PyPOTS community and chat on , or create an issue. If you have any additional questions or have interests in collaboration, please take a look at my GitHub profile and feel free to contact me 🤝.

Thank you all for your attention! 😃

[^1]: Du, W., Cote, D., & Liu, Y. (2023). SAITS: Self-Attention-based Imputation for Time Series. 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. NeurIPS 2017. [^3]: Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018). BRITS: Bidirectional Recurrent Imputation for Time Series. NeurIPS 2018. [^4]: Che, Z., Purushotham, S., Cho, K., Sontag, D.A., & Liu, Y. (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports. [^5]: Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022). Graph-Guided Network for Irregularly Sampled Multivariate Time Series. ICLR 2022. [^6]: Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021). Learning Representations for Incomplete Time Series Clustering. AAAI 2021. [^7]: Jong, J.D., Emon, M.A., Wu, P., Karki, R., Sood, M., Godard, P., Ahmad, A., Vrooman, H.A., Hofmann-Apitius, M., & Fröhlich, H. (2019). Deep learning for clustering of multivariate clinical patient trajectories with missing values. GigaScience. [^8]: Chen, X., & Sun, L. (2021). Bayesian Temporal Factorization for Multidimensional Time Series Prediction. IEEE transactions on pattern analysis and machine intelligence.

🏠 Visits