LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918066310348800 |
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| author | Fons, Elizabeth Sztrajman, Alejandro El-Laham, Yousef Ferrer, Luciana Vyetrenko, Svitlana Veloso, Manuela |
| author_facet | Fons, Elizabeth Sztrajman, Alejandro El-Laham, Yousef Ferrer, Luciana Vyetrenko, Svitlana Veloso, Manuela |
| contents | Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Transform (FFT) which assumes uniform sampling, therefore requiring prior interpolation that can distort the spectra. To address this limitation, we introduce a differentiable Lomb--Scargle layer that enables a reliable computation of the power spectrum of irregularly sampled data. We integrate this layer into a novel score-based diffusion model (LSCD) for time series imputation conditioned on the entire signal spectrum. Experiments on synthetic and real-world benchmarks demonstrate that our method recovers missing data more accurately than purely time-domain baselines, while simultaneously producing consistent frequency estimates. Crucially, our method can be easily integrated into learning frameworks, enabling broader adoption of spectral guidance in machine learning approaches involving incomplete or irregular data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation Fons, Elizabeth Sztrajman, Alejandro El-Laham, Yousef Ferrer, Luciana Vyetrenko, Svitlana Veloso, Manuela Machine Learning Artificial Intelligence Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Transform (FFT) which assumes uniform sampling, therefore requiring prior interpolation that can distort the spectra. To address this limitation, we introduce a differentiable Lomb--Scargle layer that enables a reliable computation of the power spectrum of irregularly sampled data. We integrate this layer into a novel score-based diffusion model (LSCD) for time series imputation conditioned on the entire signal spectrum. Experiments on synthetic and real-world benchmarks demonstrate that our method recovers missing data more accurately than purely time-domain baselines, while simultaneously producing consistent frequency estimates. Crucially, our method can be easily integrated into learning frameworks, enabling broader adoption of spectral guidance in machine learning approaches involving incomplete or irregular data. |
| title | LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.17039 |