title: Population Aware Diffusion for Time Series Generation
summary: Diffusion models have shown promising ability in generating high-quality time
  series (TS) data. Despite the initial success, existing works mostly focus on
  the authenticity of data at the individual level, but pay less attention to
  preserving the population-level properties on the entire dataset. Such
  population-level properties include value distributions for each dimension and
  distributions of certain functional dependencies (e.g., cross-correlation, CC)
  between different dimensions. For instance, when generating house energy
  consumption TS data, the value distributions of the outside temperature and the
  kitchen temperature should be preserved, as well as the distribution of CC
  between them. Preserving such TS population-level properties is critical in
  maintaining the statistical insights of the datasets, mitigating model bias,
  and augmenting downstream tasks like TS prediction. Yet, it is often overlooked
  by existing models. Hence, data generated by existing models often bear
  distribution shifts from the original data. We propose Population-aware
  Diffusion for Time Series (PaD-TS), a new TS generation model that better
  preserves the population-level properties. The key novelties of PaD-TS include
  1) a new training method explicitly incorporating TS population-level property
  preservation, and 2) a new dual-channel encoder model architecture that better
  captures the TS data structure. Empirical results in major benchmark datasets
  show that PaD-TS can improve the average CC distribution shift score between
  real and synthetic data by 5.9x while maintaining a performance comparable to
  state-of-the-art models on individual-level authenticity.
paper_authors._id: 677745822519a7b849ccec01
paper_authors.name: Yang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-03T14:03:46.231Z
paper_authors._id: 62785fda0c66a9124a01a2c7
paper_authors.avatarUrl: /avatars/0bb2c76d7c05e0e03a832317458a7f15.svg
paper_authors.fullname: Yang Li
paper_authors.type: user
paper_authors.user: littlestone111
paper_authors._id: 677745822519a7b849ccec02
paper_authors.name: Han Meng
paper_authors._id: 677745822519a7b849ccec03
paper_authors.name: Zhenyu Bi
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-01-03T02:03:47.856Z
paper_authors._id: 65429721c9483f8861915c81
paper_authors.avatarUrl: /avatars/67d93b9e337874512805f203e01fc7b7.svg
paper_authors.fullname: Zhenyu Bi
paper_authors.type: user
paper_authors.user: bzyzz
paper_authors._id: 677745822519a7b849ccec04
paper_authors.name: Ingolv T. Urnes
paper_authors._id: 677745822519a7b849ccec05
paper_authors.name: Haipeng Chen
paper_title: Population Aware Diffusion for Time Series Generation
paper_summary: Diffusion models have shown promising ability in generating high-quality time
  series (TS) data. Despite the initial success, existing works mostly focus on
  the authenticity of data at the individual level, but pay less attention to
  preserving the population-level properties on the entire dataset. Such
  population-level properties include value distributions for each dimension and
  distributions of certain functional dependencies (e.g., cross-correlation, CC)
  between different dimensions. For instance, when generating house energy
  consumption TS data, the value distributions of the outside temperature and the
  kitchen temperature should be preserved, as well as the distribution of CC
  between them. Preserving such TS population-level properties is critical in
  maintaining the statistical insights of the datasets, mitigating model bias,
  and augmenting downstream tasks like TS prediction. Yet, it is often overlooked
  by existing models. Hence, data generated by existing models often bear
  distribution shifts from the original data. We propose Population-aware
  Diffusion for Time Series (PaD-TS), a new TS generation model that better
  preserves the population-level properties. The key novelties of PaD-TS include
  1) a new training method explicitly incorporating TS population-level property
  preservation, and 2) a new dual-channel encoder model architecture that better
  captures the TS data structure. Empirical results in major benchmark datasets
  show that PaD-TS can improve the average CC distribution shift score between
  real and synthetic data by 5.9x while maintaining a performance comparable to
  state-of-the-art models on individual-level authenticity.
paper_ai_keywords: diffusion models time series (TS) data value distributions cross-correlation (CC) Population-aware Diffusion for Time Series (PaD-TS) dual-channel encoder distribution shifts synthetic data average CC distribution shift score state-of-the-art models