metaforecast

metaforecast is a Python package for time series forecasting using meta-learning and data-centric techniques.

This package implements various techniques to enhance forecasting performance through model combination, data augmentation, and adaptive learning, building upon Nixtla’s awesome ecosystem of state-of-the-art forecasting methods.

Dynamic Ensembles

Combines multiple forecasting models using adaptive weighting strategies:

• Online learning with exponential and polynomial weights

• Performance-based dynamic model selection and trimming

• Predicted weights based on meta-learning

Synthetic Time Series Generation

Creates synthetic time series data for augmentation and testing:

• Pure synthetic generation through kernel methods

• Semi-synthetic generation preserving the patterns of a source dataset

• Transformation-based augmentation, by applying relevant operations to a given dataset

• Online augmentation during model training

Long-Horizon Meta-Learning

Improves multi-step forecasting accuracy through instance-based approaches:

• Trajectory-based nearest neighbor matching

Contents:

• Dynamic Ensembles
  • ADE
  • MLForecastADE
  • MLewa
  • MLpol
  • LossOnTrain
  • BestOnTrain
  • EqAverage
  • Windowing
• Synthetic Time Series Generation
• Long-horizon forecasting
  • MLForecastFTN
• Tutorials
  • Introduction to Dynamic Ensembles
  • Dynamic Ensembles: Comparing Several Approaches
  • Long-horizon Forecasting with FTN
  • Leveraging a Weighted FTN
  • Data Augmentation for Improved Forecasting Accuracy
  • Online Data Augmentation using Callbacks