gingado, a LinkageX resource

gingado, a LinkageX resource

gingado is a machine learning library focused on economics and finance use cases. This package aims to be suitable for beginners and advanced users alike. Use cases may range from programmatic data retrievals using SDMX to experimentation with machine learning-based econometric estimators to more complex forecasting pipelines used in production.

What is gingado?

gingado seeks to facilitate the use of machine learning in economic and finance use cases, while promoting good practices. This package aims to be suitable for beginners and advanced users alike. Use cases may range from simple data retrievals to experimentation with machine learning algorithms to more complex model pipelines used in production. gingado is developed as part of the sdmx.io project under the BIS Open Tech initiative.

Overview

gingado is a free, open source library with different functionalities:

  • data augmentation, to add more data from official sources using SDMX, improving the machine models being trained by the user;
  • relevant datasets, both real and simulated, to allow for easier model development and comparison;
  • automatic benchmark model, to assess candidate models against a reasonably well-performant model;
  • machine learning-based estimators, to help answer questions of academic or practical importance;
  • support for model documentation, to embed documentation and ethical considerations in the model development phase; and
  • utilities, including tools to allow for lagging variables in a straightforward way.

Each of these functionalities builds on top of the previous one. They can be used on a stand-alone basis, together, or even as part of a larger pipeline from data input to model training to documentation!

New functionalities are planned over time, so consider checking frequently on gingado for the latest toolsets.

Design Principles

The choices made during development of gingado derive from the following principles, in no particular order:

  • flexibility: users can use gingado out of the box or build custom processes on top of it;
  • compatibility: gingado works well with other widely used libraries in machine learning, such as scikit-learn and pandas, but also with SDMX compliant REST endpoints for retrieving data; and
  • responsibility: gingado facilitates and promotes model documentation, including ethical considerations, as part of the machine learning development workflow.

For more infornation, please refer to the gingado project pages on GitHub (external link): https://bis-med-it.github.io/gingado/