Metadata-Version: 2.3
Name: metaforecast
Version: 0.1.4
Summary: Meta-learning and Data-centric Forecasting
Project-URL: Homepage, https://github.com/vcerqueira/metaforecast
Project-URL: Bug Tracker, https://github.com/vcerqueira/metaforecast/issues
Author-email: Vitor Cerqueira <cerqueira.vitormanuel@gmail.com>
Keywords: Data Science,Forecasting,Machine Learning,Time Series
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Requires-Dist: lightgbm==4.5.0
Requires-Dist: mlforecast==0.13.4
Requires-Dist: neuralforecast==1.7.5
Requires-Dist: tslearn==0.6.3
Description-Content-Type: text/markdown

# metaforecast: Meta-Learning and Data-Centric AI for Actionable Forecasting

metaforecast is a Python package that combines meta-learning techniques with data-centric AI approaches to provide powerful and actionable forecasting capabilities. Built on top of the Nixtla ecosystem, this package offers advanced tools for time series analysis and prediction.

## Features

MetaForecast currently consists of three main modules:

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

## Installation

You can install metaforecast using pip:

```
pip install metaforecast
```

## Quick Start

todo

## Documentation

For detailed documentation, please visit todo

## Examples

Check out the `notebooks` folder (currently under construction) for example usage and tutorials. 

## Dependencies

metaforecast is built on top of the Nixtla ecosystem. 


