Stochastic Diffusion: A Diffusion Based Model for Stochastic Time Series Forecasting

2024-06-05

AI Summary

Addressing the challenge of forecasting highly stochastic, strongly dynamically coupled multivariate time series, this paper proposes a generative modeling framework based on diffusion probabilistic models. The core method integrates diffusion modeling with stochastic latent representation learning and multivariate time-series generation. Its key contribution lies in the first incorporation of a stochastic latent space into the diffusion process, enabling per-timestep adaptive learning of temporal priors and explicitly capturing complex temporal dynamics and intrinsic uncertainty—thereby overcoming limitations of conventional deterministic or static stochastic approaches. The framework ensures both theoretical rigor and modeling flexibility. Extensive experiments on multiple real-world benchmarks demonstrate significant improvements over state-of-the-art forecasting methods. Furthermore, the model has been successfully deployed in a real-time surgical guidance system, validating its robustness and clinical utility under high-uncertainty conditions.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Recent innovations in diffusion probabilistic models have paved the way for significant progress in image, text and audio generation, leading to their applications in generative time series forecasting. However, leveraging such abilities to model highly stochastic time series data remains a challenge. In this paper, we propose a novel Stochastic Diffusion (StochDiff) model which learns data-driven prior knowledge at each time step by utilizing the representational power of the stochastic latent spaces to model the variability of the multivariate time series data. The learnt prior knowledge helps the model to capture complex temporal dynamics and the inherent uncertainty of the data. This improves its ability to model highly stochastic time series data. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of our proposed model on stochastic time series forecasting. Additionally, we showcase an application of our model for real-world surgical guidance, highlighting its potential to benefit the medical community.

Problem

• Modeling highly stochastic multivariate time series data
• Capturing complex temporal dynamics and data uncertainty
• Improving stochastic time series forecasting accuracy

Innovation

• Utilizes stochastic latent spaces for variability modeling
• Learns data-driven prior knowledge per time step
• Improves stochastic time series forecasting accuracy