repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 12d 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 →
- FuzzySimilar title/name (fuzzy) · 84%DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging →
“Fuzzy title match (0.92): “DARTS: Decoder-Aware Representation Tuning via Surgery for M” ≈ “unit8co/darts””
- PossiblePossibly related (embedding) · 50%Dynamic rough set learning for reliable early warning in industrial time-series systems - Nature →
- PossiblePossibly related (embedding) · 48%New taxonomy unifies deep-learning methods for detecting anomalies in multivariate time series - Bioengineer.org →
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 ForecastingpaperDARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging
Covers (incoming)
newsDynamic rough set learning for reliable early warning in industrial time-series systems - NaturenewsNew taxonomy unifies deep-learning methods for detecting anomalies in multivariate time series - Bioengineer.orgnewsIBM Granite Time Series models bring real-time forecasting and anomaly detection to Confluent Cloud - IBM
Related across the graph
paperExtreme Adaptive Transformer for Time Series ForecastingnewsIBM Granite Time Series models bring real-time forecasting and anomaly detection to Confluent Cloud - IBMnewsNew taxonomy unifies deep-learning methods for detecting anomalies in multivariate time series - Bioengineer.orgpaperCAAD: 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?paperDARTS: Decoder-Aware Representation Tuning via Surgery for Model MergingnewsDynamic rough set learning for reliable early warning in industrial time-series systems - NaturepaperForecasting With LLMs: Improved Generalization Through Feature Steering