Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models

• URL: http://arxiv.org/abs/2508.21330v1
• Date: Fri, 29 Aug 2025 05:10:10 GMT
• Title: Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models
• Abstract summary: We propose StageDiff, a staged generative model for long-term time series based on diffusion models.<n>First, through stage-wise sequence generation and inter-stage information transfer, the model preserves long-term sequence dependencies.<n>Second, within each stage, progressive sequence decomposition is applied to perform channel-independent modeling at different time scales.
• Score: 13.345872524896722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models have been successfully used in the field of time series generation. However, when dealing with long-term time series, which span over extended periods and exhibit more complex long-term temporal patterns, the task of generation becomes significantly more challenging. Long-term time series exhibit long-range temporal dependencies, but their data distribution also undergoes gradual changes over time. Finding a balance between these long-term dependencies and the drift in data distribution is a key challenge. On the other hand, long-term time series contain more complex interrelationships between different feature sequences, making the task of effectively capturing both intra-sequence and inter-sequence dependencies another important challenge. To address these issues, we propose Stage-Diff, a staged generative model for long-term time series based on diffusion models. First, through stage-wise sequence generation and inter-stage information transfer, the model preserves long-term sequence dependencies while enabling the modeling of data distribution shifts. Second, within each stage, progressive sequence decomposition is applied to perform channel-independent modeling at different time scales, while inter-stage information transfer utilizes multi-channel fusion modeling. This approach combines the robustness of channel-independent modeling with the information fusion advantages of multi-channel modeling, effectively balancing the intra-sequence and inter-sequence dependencies of long-term time series. Extensive experiments on multiple real-world datasets validate the effectiveness of Stage-Diff in long-term time series generation tasks.

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