Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer

The analysis of wearable-sensory time series data (e.g., heart rate records) benefits many applications (e.g., activity recognition, disease diagnosis). However, sensor measurements usually contain missing values due to various factors (e.g., user behavior, lack of charging), which may degrade the performance of downstream analytical tasks (e.g., regression, prediction). Thus, time series imputation is desired, which is capable of making sensory time series complete. Existing time series imputation methods generally employ various deep neural network models (e.g., GRU and GAN) to fill missing values by leveraging temporal patterns extracted from the contextual observations. Despite their effectiveness, we argue that most existing models can only achieve sub-optimal imputation performance due to the fact that they are inherently limited in sharing only one single set of model parameters to perform imputation on all individuals. Relying on one set of parameters limits the expressiveness of the imputation model as such models are bound to fail in capturing various complex personal characteristics. Therefore, most existing models tend to achieve inferior imputation performance, especially when a long duration of missing values, i.e., a large gap, is observed in the time series data. To address the limitation, this work develops a new imputation framework--Personalized Wearable-Sensory Time Series Imputation framework (PTSI) to provide a fully personalized treatment for time series imputation via effective knowledge transfer. In particular, PTSI first leverages a meta-learning paradigm to learn a well-generalized initialization to facilitate the adaption process for each user. To make the time series imputation be reflective of an individual's unique characteristics, we further endow PTSI with the capability of learning personalized model parameters, which is achieved by designing a parameter initialization modulating component. Extensive experiments on real-world human heart rate datasets demonstrate that our PTSI framework outperforms various state-of-the-art methods by a large margin consistently.