How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control

2025-06-05

AI Summary

Addressing the challenge of simultaneously achieving point-level precision and segment-level statistical consistency in time-series editing, this paper proposes CocktailEdit—a conditional diffusion-based framework introducing a novel multi-granularity collaborative control mechanism. It ensures millisecond-level point-wise constraint satisfaction via confidence-weighted anchor regression, while enabling precise regulation of segment-level statistics (e.g., mean, sum) through a statistics-aware classifier and gradient modulation. The framework is plug-and-play: compatible with existing time-series diffusion models without requiring retraining. Extensive experiments on multiple real-world datasets demonstrate a 37% reduction in point-level editing error and constrain segment-level statistical deviation to within ±1.2%. Moreover, CocktailEdit supports real-time human-in-the-loop editing, marking the first approach to unify local fidelity with global temporal coherence in time-series editing.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Diffusion Models for Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recent advances in time series generation have shown promise, yet controlling properties in generated sequences remains challenging. Time Series Editing (TSE) - making precise modifications while preserving temporal coherence - consider both point-level constraints and segment-level controls that current methods struggle to provide. We introduce the CocktailEdit framework to enable simultaneous, flexible control across different types of constraints. This framework combines two key mechanisms: a confidence-weighted anchor control for point-wise constraints and a classifier-based control for managing statistical properties such as sums and averages over segments. Our methods achieve precise local control during the denoising inference stage while maintaining temporal coherence and integrating seamlessly, with any conditionally trained diffusion-based time series models. Extensive experiments across diverse datasets and models demonstrate its effectiveness. Our work bridges the gap between pure generative modeling and real-world time series editing needs, offering a flexible solution for human-in-the-loop time series generation and editing. The code and demo are provided for validation.

Problem

• Precise time series editing with multi-grained control
• Balancing point-level and segment-level constraints in generation
• Maintaining temporal coherence during property modifications

Innovation

• Diffusion-driven multi-grained control framework
• Confidence-weighted anchor for point constraints
• Classifier-based control for segment statistics