  Missing value is a very common and unavoidable problem in sensors, and
researchers have made numerous attempts for missing value imputation,
particularly in deep learning models. However, for real sensor data, the
specific data distribution and data periods are rarely considered, making it
difficult to choose the appropriate evaluation indexes and models for different
sensors. To address this issue, this study proposes a multistage imputation
framework based on deep learning with adaptability for missing value
imputation. The model presents a mixture measurement index of low- and
higher-order statistics for data distribution and a new perspective on data
imputation performance metrics, which is more adaptive and effective than the
traditional mean squared error. A multistage imputation strategy and dynamic
data length are introduced into the imputation process for data periods.
Experimental results on different types of sensor data show that the multistage
imputation strategy and the mixture index are superior and that the effect of
missing value imputation has been improved to some extent, particularly for the
large segment imputation problem. The codes and experimental results have been
uploaded to GitHub.
