The Rise of Diffusion Models in Time-Series Forecasting

2024/01/05 by Caspar Meijer, Meijer, Caspar, Lydia Y. Chen +1 · 8 citations
Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2401.03006

openalex publication_date 2024/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This survey delves into the application of diffusion models in time-series forecasting. Diffusion models are demonstrating state-of-the-art results in various fields of generative AI. The paper includes comprehensive background information on diffusion models, detailing their conditioning methods and reviewing their use in time-series forecasting. The analysis covers 11 specific time-series implementations, the intuition and theory behind them, the effectiveness on different datasets, and a comparison among each other. Key contributions of this work are the thorough exploration of diffusion models' applications in time-series forecasting and a chronologically ordered overview of these models. Additionally, the paper offers an insightful discussion on the current state-of-the-art in this domain and outlines potential future research directions. This serves as a valuable resource for researchers in AI and time-series analysis, offering a clear view of the latest advancements and future potential of diffusion models.

Cited by

• High-Performance Self-Supervised Learning by Joint Training of Flow Matching
• EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting
• Structured Noise Modeling for Enhanced Time-Series Forecasting
• CSI Prediction Using Diffusion Models
• Valuation of Exotic Options and Counterparty Games Based on Conditional Diffusion
• RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting
• STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
• VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones

Related

• Diffusion Models for Time Series Forecasting: A Survey
• Diffusion Models for Time Series Applications: A Survey
• Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
• A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
• Auto-Regressive Moving Diffusion Models for Time Series Forecasting