Binned Data Provide Better Imputation of Missing Time Series Data from Wearables.

ABSTRACT: The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better impu

SUBMITTER: Chakrabarti S 

PROVIDER: S-EPMC9919790 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature