ProtoFlow: Prototype-Guided Flow Matching for Multivariate Time Series Forecasting

arXiv:2610.01320 · cs.AI · Submitted 2026-10-01 · Read on arXiv

cs.AI

Submitted: 2026-10-01

Updated: 2026-10-01

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Flow Matching — 1× in this paper · explained in 10 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes

Sources

• TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
• Density estimation using Real NVP
• Probabilistic Forecasting via Autoregressive Flow Matching
• MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
• HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
• Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models
• Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting
• Vector Quantized Time Series Generation with a Bidirectional Prior Model
• SDFlow: Similarity-Driven Flow Matching for Time Series Generation
• Flow Matching for Generative Modeling
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• The Rise of Diffusion Models in Time-Series Forecasting
• Vision-Enhanced Time Series Forecasting via Latent Diffusion Models
• Generalization in Generation: A closer look at Exposure Bias
• Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
• Score-Based Generative Modeling through Stochastic Differential Equations
• TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
• Improving and generalizing flow-based generative models with minibatch optimal transport
• TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation
• Non-stationary Diffusion For Probabilistic Time Series Forecasting

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