The Time Machine: Navigating Historic and Future Data Space - AI Focus
Poster and Trailer
Description
We live in a time of radical changes in our environment and rapid biodiversity loss. To counteract these processes, science communication needs to be improved by means of an easily understandable visualization. However, this produces one key issue: Most datasets are often only “snapshots” of specific moments in time, i.e. they contain missing years. Therefore, we present a dedicated tool to solve this issue: Simply train your own machine-learning model and use it to impute those gaps in your time series. No coding proficiency required.
