TIMED: Adversarial and Autoregressive Refinement of Diffusion-Based Time Series Generation

2025-09-23

AI Summary

To address the scarcity, high noise levels, and costly acquisition of real-world time series data, this paper proposes a unified synthetic framework integrating diffusion-based generation, autoregressive modeling, and adversarial discrimination. The method jointly models the marginal distribution of observations and their conditional temporal dependencies: a denoising diffusion probabilistic model (DDPM) captures global temporal structure; a teacher-forced autoregressive network enforces local dynamic consistency; and a Wasserstein critic combined with maximum mean discrepancy (MMD) loss aligns real and synthetic distributions in feature space. Additionally, masked attention enhances long-range dependency modeling. Evaluated on multiple multivariate time series benchmarks, our approach significantly outperforms existing generative models, producing samples with high fidelity and strong temporal coherence. Consequently, the synthesized data substantially improves performance in downstream tasks such as forecasting and anomaly detection.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Generation
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Generating high-quality synthetic time series is a fundamental yet challenging task across domains such as forecasting and anomaly detection, where real data can be scarce, noisy, or costly to collect. Unlike static data generation, synthesizing time series requires modeling both the marginal distribution of observations and the conditional temporal dependencies that govern sequential dynamics. We propose TIMED, a unified generative framework that integrates a denoising diffusion probabilistic model (DDPM) to capture global structure via a forward-reverse diffusion process, a supervisor network trained with teacher forcing to learn autoregressive dependencies through next-step prediction, and a Wasserstein critic that provides adversarial feedback to ensure temporal smoothness and fidelity. To further align the real and synthetic distributions in feature space, TIMED incorporates a Maximum Mean Discrepancy (MMD) loss, promoting both diversity and sample quality. All components are built using masked attention architectures optimized for sequence modeling and are trained jointly to effectively capture both unconditional and conditional aspects of time series data. Experimental results across diverse multivariate time series benchmarks demonstrate that TIMED generates more realistic and temporally coherent sequences than state-of-the-art generative models.

Problem

• Generating high-quality synthetic time series with realistic temporal dependencies
• Modeling both marginal distributions and conditional sequential dynamics in data
• Ensuring temporal smoothness and fidelity in synthetic time series generation

Innovation

• Denoising diffusion model captures global structure
• Supervisor network learns autoregressive temporal dependencies
• Wasserstein critic and MMD loss ensure fidelity