2023

Regular Time-series Generation using SGM

Lim, Haksoo, Kim, Minjung, Park, Sewon et al.

Understand

Score-based generative models (SGMs) are generative models that are in the spotlight these days.

Time-series frequently occurs in our daily life, e.g., stock data, climate data, and so on.
Especially, time-series forecasting and classification are popular research topics in the field of machine learning.
SGMs are also known for outperforming other generative models.

Built on

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Then

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AI4I 2020 Predictive Maintenance Dataset
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Later among the works it cites.
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Original
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Original
Jolicoeur-Martineau, A.; Li, K.; Piché-Taillefer, R.; Kachman, T.; and Mitliagkas, I. 2021 · 2021
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Symbolic Music Generation with Diffusion Models
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Mittal, G.; Engel, J. H.; Hawthorne, C.; and Simon, I. 2021 · 2021
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Tashiro, Y.; Song, J.; Song, Y.; and Ermon, S. 2021 · 2021
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Yan, T.; Zhang, H.; Zhou, T.; Zhan, Y.; and Xia, Y. 2021 · 2021
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Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Xiao, Z.; Kreis, K.; and Vahdat, A. 2022 · 2022
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