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  1. Home
  2. /Repositories
  3. /Nixtla/mlforecast
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repoGitHubTrust 82 Β· PrimaryPublished 2d agoLive Β· 2d ago

Nixtla/mlforecast

Scalable machine πŸ€– learning for time series forecasting.

Lineage graph

Paper β†’ model β†’ 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 β€” so bad links are debuggable.

  • PossiblePossibly related (embedding) Β· 61%Best machine learning development companies for time series forecasting (2026) - PC Tech Magazine β†’
  • PossiblePossibly related (embedding) Β· 60%How Good Can Linear Models Be for Time-Series Forecasting? β†’
  • PossiblePossibly related (embedding) Β· 53%GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting β†’
  • PossiblePossibly related (embedding) Β· 52%TiRex-2: Generalizing TiRex to Multivariate Data and Streaming β†’
  • PossiblePossibly related (embedding) Β· 52%Self-Gating Attention for Efficient Time Series Forecasting β†’
  • PossiblePossibly related (embedding) Β· 45%Going Beyond Statistical Forecasts to Estimate Peak Season Demand Through Machine Learning - Supply & Demand Chain Executive β†’
  • PossiblePossibly related (embedding) Β· 46%How does a 102M-parameter transformer forecast multivariate time series? β†’
  • PossiblePossibly related (embedding) Β· 45%Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty β†’

Covers

newsBest machine learning development companies for time series forecasting (2026) - PC Tech Magazine

Implements

paperHow Good Can Linear Models Be for Time-Series Forecasting?paperGatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series ForecastingpaperTiRex-2: Generalizing TiRex to Multivariate Data and StreamingpaperSelf-Gating Attention for Efficient Time Series Forecasting

Covers (incoming)

newsGoing Beyond Statistical Forecasts to Estimate Peak Season Demand Through Machine Learning - Supply & Demand Chain ExecutivenewsHow does a 102M-parameter transformer forecast multivariate time series?

Implements (incoming)

paperLearning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

Related across the graph

newsBest machine learning development companies for time series forecasting (2026) - PC Tech MagazinepaperGatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series ForecastingnewsGoing Beyond Statistical Forecasts to Estimate Peak Season Demand Through Machine Learning - Supply & Demand Chain ExecutivepaperTiRex-2: Generalizing TiRex to Multivariate Data and StreamingpaperLearning-based Probabilistic Load Forecasting with Post-hoc and In-model UncertaintynewsHow does a 102M-parameter transformer forecast multivariate time series?paperHow Good Can Linear Models Be for Time-Series Forecasting?paperSelf-Gating Attention for Efficient Time Series Forecasting
Knowledge path·NBest machine learning development companies for time series forecasting (2026) - PC Tech Magazine→PGatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting→NGoing Beyond Statistical Forecasts to Estimate Peak Season Demand Through Machine Learning - Supply & Demand Chain Executive→RNixtla/mlforecast

Topics

daskforecastforecastinglightgbmmachine-learningpythontime-seriesxgboost

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