Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization
Lee Mason, Amy Berrington de González, Montserrat García‐Closas, Stephen Jacob Chanock, Blanaid M. Hicks, Jonas S. Almeida · medRxiv · 2022
Abstract Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it allows the user to complement the forecasts with their own knowledge, a process which leads to more applicable results. In general, mechanistic methods are more interpretable than non-mechanistic methods, but they require explicit knowledge of the underlying dynamics. In this paper, we introduce a tool which performs interpretable, non-mechanistic forecasts using interactive visualization and a simple, data-focused forecasting technique. To ensure the work is FAIR and privacy is ensured, we have released the tool as an entirely in-browser web-application.