NeurIPS2021SAITS

NeurIPS2021SAITS's starred repositories

PyGrinder

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 random), MAR (at random), MNAR (not at random), sub sequence missing, and block missing

Language:PythonBSD-3-Clause2800

TSDB

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, and etc.

Language:PythonBSD-3-Clause14300

BrewPOTS

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

Language:Jupyter NotebookBSD-3-Clause4900

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 imputation, classification, clustering, forecasting, & anomaly detection on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values

Language:PythonBSD-3-Clause91300

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-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

Language:PythonMIT30100