metaforecast 0.2.2

Meta-learning and Data-centric Forecasting

metaforecast

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

This package implements various techniques to improve forecasting accuracy based on dynamic model combination, data augmentation, and adaptive learning, building upon Nixtla’s awesome ecosystem of state-of-the-art forecasting methods.

Features

metaforecast currently consists of three main modules:

1. Dynamic Ensembles: Combining multiple models with adaptive ensemble techniques.
2. Synthetic Time Series Generation: Creating realistic synthetic time series data for robust model training and testing. Includes a special callback for online data augmentation.
3. Long-Horizon Meta-Learning: Instance-based meta-learning for multi-step forecasting.

Installation

You can install metaforecast using pip:

pip install metaforecast

[Optional] Installation from source

To install metaforecast from source, clone the repository and run the following command:

git clone https://github.com/vcerqueira/metaforecast
pip install -e metaforecast
cd metaforecast
pre-commit install

Documentation

Check the documentation for the API reference and module descriptions. You can get started with a few tutorials.

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⚠️ WARNING

metaforecast is in the early stages of development. The codebase may undergo significant changes. If you encounter any issues, please report them in GitHub Issues