Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs
📰 ArXiv cs.AI
Graph2TS generates time series data while preserving global temporal structure and modeling local variations using Quantile-Graph VAEs
Action Steps
- Identify the limitations of existing time series generation models in preserving global temporal structure and modeling local variations
- Propose a new framework using Quantile-Graph VAEs to address these limitations
- Implement the Graph2TS model to generate time series data with controlled structure
- Evaluate the performance of Graph2TS in preserving temporal patterns and modeling stochastic variations
Who Needs to Know This
Data scientists and AI engineers working on time series generation and analysis can benefit from this research, as it provides a new approach to modeling complex temporal patterns
Key Insight
💡 Graph2TS resolves the tension between preserving global temporal structure and modeling local variations in time series generation
Share This
📈 Generate time series data with controlled structure using Graph2TS and Quantile-Graph VAEs!
Key Takeaways
Graph2TS generates time series data while preserving global temporal structure and modeling local variations using Quantile-Graph VAEs
Full Article
Title: Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs
Abstract:
arXiv:2603.19970v1 Announce Type: cross Abstract: Although recent generative models can produce time series with close marginal distributions, they often face a fundamental tension between preserving global temporal structure and modeling stochastic local variations, particularly for highly volatile signals with weak or irregular periodicity. Direct distribution matching in such settings can amplify noise or suppress meaningful temporal patterns. In this work, we propose a structure-residual per
Abstract:
arXiv:2603.19970v1 Announce Type: cross Abstract: Although recent generative models can produce time series with close marginal distributions, they often face a fundamental tension between preserving global temporal structure and modeling stochastic local variations, particularly for highly volatile signals with weak or irregular periodicity. Direct distribution matching in such settings can amplify noise or suppress meaningful temporal patterns. In this work, we propose a structure-residual per
DeepCamp AI