Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask-Reference-Cited by-同舟云学术

Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask

Published:2024-08-24 Issue: Volume:31 Page:2560-2571
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Senane Zineb¹^ORCID,Cao Lele²^ORCID,Buchner Valentin Leonhard²^ORCID,Tashiro Yusuke³^ORCID,You Lei⁴^ORCID,Herman Pawel Andrzej⁵^ORCID,Nordahl Mats⁵^ORCID,Tu Ruibo⁵^ORCID,von Ehrenheim Vilhelm⁶^ORCID

Affiliation:

1. Motherbrain, EQT Group & KTH Royal Institute of Technology, Stockholm, Sweden

2. Motherbrain, EQT Group, Stockholm, Sweden

3. Mitsubishi UFJ Trust Investment Technology Institute, Tokyo, Japan

4. Technical University of Denmark, Ballerup, Denmark

5. KTH Royal Institute of Technology, Stockholm, Sweden

6. Motherbrain, EQT Group & QA.tech, Stockholm, Sweden

Funder

MOTIE

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671673

Reference106 articles.

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2. Juan Lopez Alcaraz and Nils Strodthoff. 2023. Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models. Transactions on Machine Learning Research (2023).

3. Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu. 2023. TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting. arXiv preprint arXiv:2310.04948 (2023).

4. Lele Cao, Sahar Asadi, Wenfei Zhu, Christian Schmidli, and Michael Sjöberg. 2020. Simple, scalable, and stable variational deep clustering. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 108--124.

5. Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data

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