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

500 70 +0/wk

GitHub

attention attention-mechanism deep-learning imputation imputation-model impute incomplete-data incomplete-time-series interpolation irregular-sampling machine-learning missing-values

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Star & Fork Trend (21 data points)

Stars

Forks

Multi-Source Signals

GitHub

stars 500

forks 70

Growth Velocity

WenjieDu/SAITS has +0 stars this period . Velocity data will be available after more historical data is collected.

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Metric	SAITS	bpycv	FinRL_Podracer	MatchZoo-py
Stars	500	500	500	501
Forks	70	59	122	107
Weekly Growth	+0	+0	+0	+0
Language	Python	Python	Python	Python
Sources	1	1	1	1
License	MIT	MIT	NOASSERTION	Apache-2.0

Capability Radar vs bpycv

SAITS

bpycv

Maintenance Activity 0

Last code push 190 days ago.

Community Engagement 70

Fork-to-star ratio: 14.0%. Active community forking and contributing.

Issue Burden 70

Issue data not yet available.

Growth Momentum 30

No measurable growth in the current period (first-day cold start expected).

License Clarity 95

Licensed under MIT. Permissive — safe for commercial use.

Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.