Ecosyste.ms sponsors
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Researcher on Machine Learning, Deep Learning, Data Science/Mining, Time Series Analysis, Neural Network, AI/LLM for TS, Missingness/Missing Value Imputation
Funding Links: https://github.com/sponsors/WenjieDu
I'm making my contributions to partially-observed time-series (POTS) modeling systems, benchmarks, and applications.
You can also sponsor me on:
Buy Me a Coffee - WenjieDu
Ko-fi - WenjieDu
Besides, you can sponsor me with cryptocurrencies, and here are my wallet addresses:
DOGE wallet address: D7nHiEpq1nsDQgpUHknvMGnQ9wRADRPyGQ
XMR wallet address: 85Hd35Cqg2TaW6sLA5eYBbMmC2iFDMfKtBTL6PMJMiPBXVDf5jFKr6wf1iEqzTcvhXETqWPDACTMNcHiXmQfVo8T5vU7vjk
ETH wallet address: 0x9ab544f3435fbfbc21d3ead0d25741e54372d19e
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…
Language: Python - Stars: 1118The 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…
Language: Python - Stars: 335a 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,…
Language: Python - Stars: 169PyGrinder: a Python toolkit for grinding data beans into the incomplete for real-world data simulation by introducing missing values with different missingness patterns, including MCAR (complete at…
Language: Python - Stars: 34The tutorials for PyPOTS, guide you to model partially-observed time series datasets.
Language: Jupyter Notebook - Stars: 59Awesome 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
Language: Python - Stars: 217