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Conference Paper RDMI: Recursive Training-Based Diffusion Model for Multivariate Time Series Imputation
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Authors
Yu Min Hwang, Seung-Chul Son, Nacwoo Kim, Seok-Kap Ko, Byung-Tak Lee
Issue Date
2023-06
Citation
International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) 2023, pp.846-850
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ITC-CSCC58803.2023.10212776
Abstract
In this paper, we present a novel approach for imputing missing values in multivariate time series using a recursive training-based diffusion model. Our proposed framework incorporates meta-learning, self-conditioning, and recursive training as key components to enhance imputation performance. We evaluate the model on two publicly available real-world datasets and achieve an improvement in RMSE, MAE, CRPS, MAPE, and SMAPE compared to the state-of-the-art model. Additionally, our ablation study confirms that each proposed technique has a meaningful effect on MTS imputation.
KSP Keywords
Diffusion Model, Key Components, Meta-learning, Missing values, Multivariate time series, Novel approach, Real-world, Self-conditioning, state-of-The-Art