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More awesome private repos will open source 🙃
💥
More awesome private repos will open source 🙃

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@conda-forge @PyPOTS @TimeSeries-AI

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WenjieDu/README.md

🤙 Contact info:

👋 Hi, I'm Wenjie Du (杜文杰 in Chinese). My research majors in modeling time series with machine learning, especially partially-observed time series (POTS), namely, incomplete time series with missing values, A.K.A. irregularly-sampled time series. I strongly advocate open-source and reproducible research, and I always devote myself to building my work into valuable real-world applications. Unix philosophy "Do one thing and do it well" is also my life philosophy, and I always strive to walk my talk. My research goal is to model this non-trivial and kaleidoscopic world with machine learning to make it a better place for everyone. It's my honor if my work could help you in any way.

🤔 POTS is ubiquitous in the real world and is vital to AI landing in the industry. However, it still lacks attention from academia and is also in short of a dedicated toolkit even in a community as vast as Python. Therefore, to facilitate our researchers and engineers' work related to POTS, I'm leading PyPOTS Research (pypots.com) to build a comprehensive Python toolkit ecosystem for POTS modeling, including data preprocessing, neural net training, and benchmarking. Stars🌟 on our repos are also very welcome of course if you like what we're trying to achieve with PyPOTS.

🤖 Furthermore, to rescue human beings from the tedious and time-consuming work of mass time series analysis, we are building state-of-the-art Time-Series AI (time-series.ai) for time series multitask end-to-end learning (classification, forecasting, clustering, anomaly detection), data reconstruction (A.K.A. cleaning, repairing, imputation), and data generation for privacy protection and data augmentation, which will be available soon! We also provide consulting services and tailored AI for companies and organizations that need help with time series analysis and applications.

💬 I'm open to questions related to my research and always try my best to help others. I love questioning myself and I never stop. If you have questions for discussion or have interests in collaboration, please feel free to drop me an email or ping me on LinkedIn/WeChat/Slack (contact info is at the top) 😃 You can follow me on Google Scholar and GitHub to get notified of our latest publications and open-source projects. Note that I'm very glad to help review papers related to my research, but ONLY for open-source ones with readable code.

👇 I served as a reviewer for

❤️ If you enjoy what I do, you can fund me and become a sponsor. And I assure you that every penny from sponsorships will be used to support impactful open-science research.

😊 Thank you for reading my profile. Feel free to contact me if you'd like to trigger discussions.

🏠 Visits number of profile visits

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  1. AI4TS AI4TS Public

    The client for Time-Series AI (https://time-series.ai), where we build artificial intelligence for unified time-series analysis

    Python 6

  2. PyPOTS PyPOTS Public

    A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputatio…

    Python 1.1k 108

  3. SAITS SAITS Public

    The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-s…

    Python 328 51

  4. Awesome_Imputation Awesome_Imputation Public

    Awesome Deep Learning for Time-Series Imputation, including a must-read paper list about applying neural networks to impute incomplete time series containing NaN missing values/data

    Python 209 24

  5. TSDB TSDB Public

    a Python toolbox loads 172 public time series datasets for machine/deep learning with a single line of code. Datasets from multiple domains including healthcare, financial, power, traffic, weather,…

    Python 165 17

  6. BrewPOTS BrewPOTS Public

    The tutorials for PyPOTS, guide you to model partially-observed time series datasets.

    Jupyter Notebook 56 8