Nixtla/neuralforecast

Scalable and user friendly neural :brain: forecasting algorithms.

What it solves

NeuralForecast addresses the difficulty of implementing state-of-the-art neural forecasting models. Many existing implementations are hard to use, computationally expensive, and often fail to outperform traditional statistical methods. This library provides a collection of accurate, efficient, and user-friendly neural networks for time-series forecasting.

How it works

It provides a unified interface (using a familiar .fit and .predict syntax) to access over 30 different neural forecasting models, ranging from classic RNNs (like LSTM and GRU) to modern Transformers (like PatchTST and iTransformer). It supports exogenous variables, static covariates, and probabilistic forecasting with quantile losses and parametric distributions.

Who it’s for

Data scientists and machine learning engineers who need to perform high-accuracy time-series forecasting and want to avoid the complexity of implementing these models from scratch.

Highlights

  • Extensive Model Library: Includes over 30 state-of-the-art models including NHITS, NBEATSx, and TimeLLM.
  • Automatic Optimization: Integrates with Ray and Optuna for distributed automatic hyperparameter tuning.
  • Interpretability: Offers methods to analyze trend, seasonality, and exogenous components.
  • Transfer Learning: Ability to predict with minimal history using transfer learning techniques.
  • Unified Ecosystem: Works seamlessly with StatsForecast, MLForecast, and HierarchicalForecast.

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