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

Lee Mason, Amy Berrington de Gonzalez, Montserrat Garcia-Closas, Stephen J Chanock, Blànaid Hicks and Jonas S Almeida

PLOS ONE, 2023, vol. 18, issue 4, 1-12

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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0277149

DOI: 10.1371/journal.pone.0277149

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