Keywords: conditional normalizing flows, neural odes, time series forecasting, agent forecasting
TL;DR: We produce marginal distribution as a continuous function of forecasting horizon with a conditional neural ODE flow, and apply it to predict agent positions in the PRECOG-Carla dataset.
Abstract: In this work we describe OMEN, a neural ODE based normalizing flow for the prediction of marginal distributions at flexible evaluation horizons, and apply it to agent position forecasting. OMEN's architecture embeds an assumption that marginal distributions of a given agent moving forward in time are related, allowing for an efficient representation of marginal distributions through time and allowing for reliable interpolation between prediction horizons seen in training. Experiments on a popular agent forecasting dataset demonstrate significant improvements over most baseline approaches, and comparable performance to the state of the art while providing the new functionality of reliable interpolation of predicted marginal distributions between prediction horizons as demonstrated with synthetic data.