Probabilistic Forecasting via Autoregressive Flow Matching

• URL: http://arxiv.org/abs/2503.10375v1
• Date: Thu, 13 Mar 2025 13:54:24 GMT
• Title: Probabilistic Forecasting via Autoregressive Flow Matching
• Authors: Ahmed El-Gazzar, Marcel van Gerven,
• Abstract summary: FlowTime is a generative model for probabilistic forecasting of timeseries data.<n>We decompose the joint distribution of future observations into a sequence of conditional densities, each modeled via a shared flow.<n>We demonstrate the effectiveness of FlowTime on multiple dynamical systems and real-world forecasting tasks.
• Score: 1.5467259918426441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose FlowTime, a generative model for probabilistic forecasting of multivariate timeseries data. Given historical measurements and optional future covariates, we formulate forecasting as sampling from a learned conditional distribution over future trajectories. Specifically, we decompose the joint distribution of future observations into a sequence of conditional densities, each modeled via a shared flow that transforms a simple base distribution into the next observation distribution, conditioned on observed covariates. To achieve this, we leverage the flow matching (FM) framework, enabling scalable and simulation-free learning of these transformations. By combining this factorization with the FM objective, FlowTime retains the benefits of autoregressive models -- including strong extrapolation performance, compact model size, and well-calibrated uncertainty estimates -- while also capturing complex multi-modal conditional distributions, as seen in modern transport-based generative models. We demonstrate the effectiveness of FlowTime on multiple dynamical systems and real-world forecasting tasks.

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