repoGitHubTrust 82 · PrimaryPublished 2d agoLive · 32m ago
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 53%CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency →
- PossiblePossibly related (embedding) · 48%Forecasting With LLMs: Improved Generalization Through Feature Steering →
- PossiblePossibly related (embedding) · 48%ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection →
- PossiblePossibly related (embedding) · 48%How Good Can Linear Models Be for Time-Series Forecasting? →
- PossiblePossibly related (embedding) · 47%Extreme Adaptive Transformer for Time Series Forecasting →
Implements
paperCAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal ConsistencypaperForecasting With LLMs: Improved Generalization Through Feature SteeringpaperArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly DetectionpaperHow Good Can Linear Models Be for Time-Series Forecasting?paperExtreme Adaptive Transformer for Time Series Forecasting
Related across the graph
paperExtreme Adaptive Transformer for Time Series ForecastingpaperCAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal ConsistencypaperArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly DetectionpaperHow Good Can Linear Models Be for Time-Series Forecasting?paperForecasting With LLMs: Improved Generalization Through Feature Steering