CT-OT Flow: Estimating Continuous-Time Dynamics from Discrete Temporal Snapshots
This addresses a practical challenge in fields like biology and environmental monitoring where data are only available as discrete snapshots, offering an incremental improvement over prior techniques.
The paper tackles the problem of estimating continuous-time dynamics from temporally aggregated snapshots, such as in single-cell RNA sequencing and typhoon tracking, and presents CT-OT Flow, which reduces distributional and trajectory errors compared to existing methods like OT-CFM and TrajectoryNet.
In many real-world settings--e.g., single-cell RNA sequencing, mobility sensing, and environmental monitoring--data are observed only as temporally aggregated snapshots collected over finite time windows, often with noisy or uncertain timestamps, and without access to continuous trajectories. We study the problem of estimating continuous-time dynamics from such snapshots. We present Continuous-Time Optimal Transport Flow (CT-OT Flow), a two-stage framework that (i) infers high-resolution time labels by aligning neighboring intervals via partial optimal transport (POT) and (ii) reconstructs a continuous-time data distribution through temporal kernel smoothing, from which we sample pairs of nearby times to train standard ODE/SDE models. Our formulation explicitly accounts for snapshot aggregation and time-label uncertainty and uses practical accelerations (screening and mini-batch POT), making it applicable to large datasets. Across synthetic benchmarks and two real datasets (scRNA-seq and typhoon tracks), CT-OT Flow reduces distributional and trajectory errors compared with OT-CFM, [SF]\(^{2}\)M, TrajectoryNet, MFM, and ENOT.