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I'm making my contributions to partially-observed time-series (POTS) modeling systems, benchmarks, and applications.
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- DOGE wallet address: D7nHiEpq1nsDQgpUHknvMGnQ9wRADRPyGQ
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- ETH wallet address: 0x9ab544f3435fbfbc21d3ead0d25741e54372d19e
1 sponsor has funded WenjieDu’s work.
Featured work
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WenjieDu/PyPOTS
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 844 -
WenjieDu/SAITS
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 292 -
WenjieDu/TSDB
a Python toolbox loads 172 public time series datasets for machine learning/deep learning with a single line of code. Datasets from multiple domains, e.g. healthcare, financial, power, traffic, and…
Python 134 -
WenjieDu/PyGrinder
A Python kit corrupts time series into the incomplete by introducing missing values with different missingness patterns, including MCAR (complete at random), MAR (at random) , MNAR (not at random),…
Python 27 -
WenjieDu/BrewPOTS
The tutorials for PyPOTS, guide you to model partially-observed time series datasets.
Jupyter Notebook 47 -
WenjieDu/Awesome_Imputation
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 131