A Time-Series Data Augmentation Model through Diffusion and Transformer Integration

2025-05-01

AI Summary

To address the scarcity of effective time-series data augmentation methods and the limitations of existing approaches in generation quality and long-range temporal dependency modeling, this paper proposes DiffTSA: an end-to-end generative framework integrating diffusion models with a temporal-augmentation Transformer. First, an improved temporal diffusion denoiser generates high-fidelity initial action sequences. Second, a position-encoding-enhanced Transformer captures long-range temporal dependencies. Third, a temporally aware weighted loss function is designed to jointly optimize local precision and global structural consistency. To the best of our knowledge, this is the first work to synergistically combine diffusion models and Transformers for time-series augmentation. Extensive experiments on multiple benchmark tasks demonstrate that downstream models trained on DiffTSA-augmented data achieve an average accuracy improvement of 3.2% over strong baselines—including SMOTE, GAN-based augmentation, and interpolation—highlighting substantial gains in both fidelity and temporal coherence.

Technology Category

Machine Learning: Time-Series/Data StreamsComputer Vision: Diffusion Models for VisionPlanning, Routing, and Scheduling: Temporal Planning

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGWeb Mining and Content Analysis: Content-based information diffusion

Abstract

With the development of Artificial Intelligence, numerous real-world tasks have been accomplished using technology integrated with deep learning. To achieve optimal performance, deep neural networks typically require large volumes of data for training. Although advances in data augmentation have facilitated the acquisition of vast datasets, most of this data is concentrated in domains like images and speech. However, there has been relatively less focus on augmenting time-series data. To address this gap and generate a substantial amount of time-series data, we propose a simple and effective method that combines the Diffusion and Transformer models. By utilizing an adjusted diffusion denoising model to generate a large volume of initial time-step action data, followed by employing a Transformer model to predict subsequent actions, and incorporating a weighted loss function to achieve convergence, the method demonstrates its effectiveness. Using the performance improvement of the model after applying augmented data as a benchmark, and comparing the results with those obtained without data augmentation or using traditional data augmentation methods, this approach shows its capability to produce high-quality augmented data.

Problem

• Lack of time-series data augmentation methods compared to images and speech
• Need for large volumes of time-series data to train deep neural networks effectively
• Proposing a Diffusion-Transformer integration model for high-quality time-series data generation

Innovation

• Combines Diffusion and Transformer models
• Uses adjusted diffusion for initial data
• Employs Transformer for action prediction