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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
- 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