Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schr\"odinger Bridge's End

TL;DR

This paper introduces SnapMMD, a novel method for forecasting stochastic dynamics from snapshot data by directly fitting joint distributions and inferring state-dependent volatilities, outperforming existing Schr"odinger-bridge-based approaches.

Contribution

SnapMMD is the first framework to learn unknown, state-dependent volatilities directly from snapshot data, enabling more accurate forecasting of stochastic dynamics.

Findings

SnapMMD accurately forecasts in real and synthetic experiments.

It infers unknown, state-dependent volatilities from data.

Performance at interpolation often surpasses state-of-the-art methods.

Abstract

Scientists often want to make predictions beyond the observed time horizon of "snapshot" data following latent stochastic dynamics. For example, in time course single-cell mRNA profiling, scientists have access to cellular transcriptional state measurements (snapshots) from different biological replicates at different time points, but they cannot access the trajectory of any one cell because measurement destroys the cell. Researchers want to forecast (e.g.) differentiation outcomes from early state measurements of stem cells. Recent Schr\"odinger-bridge (SB) methods are natural for interpolating between snapshots. But past SB papers have not addressed forecasting -- likely since existing methods either (1) reduce to following pre-set reference dynamics (chosen before seeing data) or (2) require the user to choose a fixed, state-independent volatility since they minimize a Kullback-Leibler divergence. Either case can lead to poor forecasting quality. In the present work, we propose a new framework, SnapMMD, that learns dynamics by directly fitting the joint distribution of both state measurements and observation time with a maximum mean discrepancy (MMD) loss. Unlike past work, our method allows us to infer unknown and state-dependent volatilities from the observed data. We show in a variety of real and synthetic experiments that our method delivers accurate forecasts. Moreover, our approach allows us to learn in the presence of incomplete state measurements and yields an $R^2$-style statistic that diagnoses fit. We also find that our method's performance at interpolation (and general velocity-field reconstruction) is at least as good as (and often better than) state-of-the-art in almost all of our experiments.