repoGitHubTrust 82 · PrimaryPublished 11d agoLive · 9d ago
OpenSTEF/openstef
Automated Machine Learning pipelines. Builds the Open Short Term Energy Forecasting package.
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) · 56%Best machine learning development companies for time series forecasting (2026) - PC Tech Magazine →
- PossiblePossibly related (embedding) · 50%Lattice Labs →
- PossiblePossibly related (embedding) · 47%How Good Can Linear Models Be for Time-Series Forecasting? →
- PossiblePossibly related (embedding) · 46%DeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85% →
- PossiblePossibly related (embedding) · 46%ipSpace.net Publishes Machine Learning Techniques Webinar - Let's Data Science →
- PossiblePossibly related (embedding) · 49%Top AI cloud platforms for deploying open source models in production, GPU AI workloads, and enterprise model training and inference - TyN Magazine →
- PossiblePossibly related (embedding) · 51%TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting →
- PossiblePossibly related (embedding) · 53%Microsoft's Aurora 1.5 AI Model Goes Open Source With Smarter Weather Forecasting - Windows Report →
Covers
newsBest machine learning development companies for time series forecasting (2026) - PC Tech MagazinenewsDeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%newsipSpace.net Publishes Machine Learning Techniques Webinar - Let's Data SciencenewsTop AI cloud platforms for deploying open source models in production, GPU AI workloads, and enterprise model training and inference - TyN Magazine
Related to
Implements
Implements (incoming)
paperTopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT ForecastingpaperLearning-based Probabilistic Load Forecasting with Post-hoc and In-model UncertaintypaperRobustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis
Covers (incoming)
newsMicrosoft's Aurora 1.5 AI Model Goes Open Source With Smarter Weather Forecasting - Windows ReportnewsIntegrating physics-based tools and machine learning for improved accuracy in city weather modeling - anl.govnewsGoing Beyond Statistical Forecasts to Estimate Peak Season Demand Through Machine Learning - Supply & Demand Chain Executive
Related across the graph
newsBest machine learning development companies for time series forecasting (2026) - PC Tech MagazinenewsGoing Beyond Statistical Forecasts to Estimate Peak Season Demand Through Machine Learning - Supply & Demand Chain ExecutivenewsDeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%newsMicrosoft's Aurora 1.5 AI Model Goes Open Source With Smarter Weather Forecasting - Windows ReportpaperLearning-based Probabilistic Load Forecasting with Post-hoc and In-model UncertaintynewsTop AI cloud platforms for deploying open source models in production, GPU AI workloads, and enterprise model training and inference - TyN MagazinenewsIntegrating physics-based tools and machine learning for improved accuracy in city weather modeling - anl.govpaperHow Good Can Linear Models Be for Time-Series Forecasting?companyLattice LabspaperRobustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable AnalysisnewsipSpace.net Publishes Machine Learning Techniques Webinar - Let's Data SciencepaperTopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting