  Deep Recurrent Neural Networks (RNN) continues to find success in predictive
decision-making with temporal event sequences. Recent studies have shown the
importance and practicality of visual analytics in interpreting deep learning
models for real-world applications. However, very limited work enables
interactions with deep learning models and guides practitioners to form
hypotheticals towards the desired prediction outcomes, especially for sequence
prediction. Specifically, no existing work has addressed the what-if analysis
and value perturbation along different time-steps for sequence outcome
prediction. We present a model-agnostic visual analytics tool, HypperSteer,
that steers hypothetical testing and allows users to perturb data for sequence
predictions interactively. We showcase how HypperSteer helps in steering
patient data to achieve desired treatment outcomes and discuss how HypperSteer
can serve as a comprehensive solution for other practical scenarios.
