RDIS: Random Drop Imputation with Self-Training for Incomplete Time Series Data

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Time-series data with missing values are a common occurrence in various fields, including healthcare, meteorology, and robotics. The process of imputation aims to fill in the missing values with valid values. Most imputation methods implicitly train models due to the presence of missing values. In this paper, we propose Random Drop Imputation with Self-training (RDIS), a novel training method for time-series data imputation models. In RDIS, we generate extra missing values by applying a random drop to the observed values in incomplete data. We can explicitly train the imputation models by filling in the missing values. Moreover, we utilize self-training with pseudo values to exploit the original missing values. To enhance the quality of pseudo values, we set a threshold and filter them based on entropy calculation. To evaluate the effectiveness of RDIS for imputing time-series data, we test it across several imputation models and obtain competitive results on three real-world datasets. © 2013 IEEE.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2023
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.11, pp.100720 - 100728

ISSN
2169-3536
DOI
10.1109/ACCESS.2023.3315343
URI
http://hdl.handle.net/10203/316146
Appears in Collection
EE-Journal Papers(저널논문)
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