Authors: Sriram Karthik Badam, Jieqiong Zhao, Niklas Elmqvist, David Ebert
Abstract: We present TimeFork, an analytics technique for predicting the behavior of multivariate time-series data originating from modern disciplines such as economics (stock market) and meteorology (climate), with human-in-the-loop. We identify two types of machine-generated predictions for such datasets: temporal prediction that predicts the future of an attribute; and spatial prediction that predicts an attribute based on the other attributes in the dataset. Visual exploration of this prediction space, constituting of predictions of different confidences, by chunking and chaining predictions over time promises accurate user-guided predictions. In order to utilize TimeFork technique, we created a visual analytics application for user-guided prediction over different time periods, thus allowing for visual exploration of time-series data.