Missing data imputing algorithm based on modified neural process

doi:10.7523/j.issn.2095-6134.2021.02.014

Abstract

Abstract: Missing data imputing is a serious problem in the field of data analysis and process, which is extremely intractable in the case of the small dataset especially. In view of this problem, a missing data imputing algorithm based on modified neural process is proposed, which can improve the imputing performance in the background of the small dataset. Firstly, the observed time series is single-represented and then obtain the symptomatic vector respectively through the neural network. Secondly, it can acquire the distribution function of the data via the neural process and introduce the correction coefficient α to determine the sampling rate more exactly based on missing rate in the training stage. Finally, it imported the imputing process and estimated the missing data via trained model. Experiments are carried out on the sea surface temperature dataset and the Beijing PM_2.5 dataset to verify the performance of the algorithm. The experiments show that the algorithm has an excellent performance in the context of small datasets, and it has a lower root mean square error compared with other algorithms.

Key words: missing data imputing, time series, modified neural process, correction coefficient

CLC Number:

TN911.7

SUN Xiaoli, GUO Yan, LI Ning, SONG Xiaoxiang. Missing data imputing algorithm based on modified neural process[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(2): 280-287.

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