부산대학교 도서관

Multivariate time-series data are abundant in many application areas, such as finance, transportation, environment, and healthcare. However, for many reasons, missing data points is a common problem, mainly associated with data collected from wearable devices. Missing values negatively impact the performance of data analysis and machine learning algorithms. Various statistical and machine-learning methods have been developed to overcome this challenge, primarily by imputation, i.e., filling in the missing values in the data. In this study, we compare some widely used classical imputation methods such as mean, median imputation, Last Observed Carried Forward (LOCF), K-Nearest Neighbors imputation (KNNI), and some recently developed techniques for time series imputation such as Bidirectional Recurrent Imputation for Time Series (BRITS), Transformer, and Self-attention-based imputation for time series (SAITS). We evaluate these methods on the Crowd-sourced Fitbit dataset on collected activity data through wearables. The results suggest that even though being a classical imputation method, KNNI can be more efficient than some state-of-the-art methods when the missing rate is low to moderate (less than 30%). Meanwhile, at a higher missing rate (greater than or equal to 30%), SAITS is the one that can give the lowest mean absolute error (MAE) with a reasonable execution time.