# metaforecast
[](https://pypi.org/project/metaforecast/)
[](https://metaforecast.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/vcerqueira/metaforecast)
[](https://pepy.tech/project/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:
```bash
pip install metaforecast
```
### [Optional] Installtion from source
To install metaforecast from source, clone the repository and run the following command:
```bash
git clone https://github.com/vcerqueira/metaforecast
pip install -e metaforecast
cd metaforecast
pre-commit install
```
## Documentation
Check the [documentation](https://metaforecast.readthedocs.io/en/latest/index.html) for
the API reference and module descriptions.
You can get started with a few [tutorials](https://metaforecast.readthedocs.io/en/latest/notebooks.html).
----
### **⚠️ 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](https://github.com/vcerqueira/metaforecast/issues)
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