[Open DMQA Seminar] Diffusion Models for Time Series

Автор: ‍김성범[ 교수 / 산업경영공학부 ]

Загружено: 2024-03-10

Просмотров: 1964

Описание: 최근 생성 분야에서 diffusion model은 높은 성능과 안정적인 생성 능력으로 많은 주목을 받으며, 다양한 분야에서 활용되고 있다. 그 중, time series 데이터에 발생한 결측치를 대체, 생성 혹은 예측하는 diffusion model 연구가 진행되고 있다. 본 세미나에서는 시계열 데이터의 특성을 반영한 diffusion model들에 대해 소개하고자 한다.

참고문헌:

[1] Lin, L., Li, Z., Li, R., Li, X., & Gao, J. (2023). Diffusion models for time-series applications: a survey. Frontiers of Information Technology & Electronic Engineering, 1-23.

[2] Rasul, K., Seward, C., Schuster, I., & Vollgraf, R. (2021, July). Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In International Conference on Machine Learning (pp. 8857-8868). PMLR.

[3] Tashiro, Y., Song, J., Song, Y., & Ermon, S. (2021). Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems, 34, 24804-24816.

[4] Alcaraz, J. M. L., & Strodthoff, N. (2022). Diffusion-based time series imputation and forecasting with structured state space models. arXiv preprint arXiv:2208.09399.

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