Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
Keywords: missing data, time series, step count, self-attention, sparsity, autocorrelation
TL;DR: We construct a novel large-scale data set and propose a novel sparse self-attention model for the problem of imputation of missing step count data.
Abstract: Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. In this work, we study the problem of imputation of missing step count data, one of the most ubiquitous forms of wearable sensor data. We construct a novel and large scale data set consisting of a training set with over 3 million hourly step count observations and a test set with over 2.5 million hourly step count observations. We propose an autocorrelation-informed sparse self-attention model for this task that captures the temporally multi-scale nature of step count data. We assess the performance of the proposed model relative to baselines based on different missing rates.
Submission Number: 13
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