Predictive analytics offers tremendous value for sustainable development (e.g. ensuring supply adequacy and preparing for contingencies), but it hinges on a high quality of time series forecasts. Bad forecasts lead to bad decisions. To achieve high quality, it is necessary to find suitable models that can deal with specific properties of the available data, and that generate results that are intuitive for decision-makers to act on.

For each domain application, achieving high quality forecasts is an innovation process that requires data scientists, IT developers and domain experts to work together. Seita performs open-source R&D activities to streamline this innovation process and to bring state-of-the-art machine learning models to work on sustainable development challenges (e.g. renewable energy production forecasts). We do this by writing tools that help you:

  1. Compare forecasting models in depth so they can be discussed within your team, by showing you how well different models do over time and how certain their forecasts are. [timely-beliefs]
  2. Integrate new forecasting models into operational production systems with ease, by facilitating the interaction between data scientists and engineers. [timetomodel]

For its R&D, Seita works together with students from Centrum Wiskunde & Informatica (CWI), the University of Amsterdam (UvA) and EIT Master Programmes.