Population Aware Diffusion for Time Series Generation
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.
677745822519a7b849ccec01
Yang Li
claimed_verified
2025-01-03T14:03:46.231Z
62785fda0c66a9124a01a2c7
/avatars/0bb2c76d7c05e0e03a832317458a7f15.svg
Yang Li
user
littlestone111
677745822519a7b849ccec02
Han Meng
677745822519a7b849ccec03
Zhenyu Bi
extracted_pending
2025-01-03T02:03:47.856Z
65429721c9483f8861915c81
/avatars/67d93b9e337874512805f203e01fc7b7.svg
Zhenyu Bi
user
bzyzz
677745822519a7b849ccec04
Ingolv T. Urnes
677745822519a7b849ccec05
Haipeng Chen
Population Aware Diffusion for Time Series Generation
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.
PaD-TS is a population-aware diffusion model for time series data generation that better preserves dataset-wide properties while maintaining individual data point authenticity.
diffusion models time series data population-level properties value distributions cross-correlation distribution shifts training method dual-channel encoder