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GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
PyGrinder: 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…
a Python toolbox for benchmarking machine learning on POTS (Partially-Observed Time Series), supporting processing pipelines of 172 public time-series datasets
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,…
[IJCAI-24] Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
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…
The tutorials for PyPOTS, guide you to model partially-observed time series datasets.
Awesome Deep Learning for Time-Series Imputation, including an unmissable paper and tool list about applying neural networks to impute incomplete time series containing NaN missing values/data
AI for time series analysis in only 1 line of code
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…
Time series forecasting with PyTorch
Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.