Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

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 EpiForecast, a tool which performs interpretable, non-mechanistic forecasts using interactive visualization and a simple, data-focused forecasting technique based on empirical dynamic modelling. EpiForecast’s primary feature is a four-plot interactive dashboard which displays a variety of information to help the user understand how the forecasts are generated. In addition to point forecasts, the tool produces distributional forecasts using a kernel density estimation method–these are visualized using color gradients to produce a quick, intuitive visual summary of the estimated future. To ensure the work is FAIR and privacy is ensured, we have released the tool as an entirely in-browser web-application.