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The ability to synthesize realistic data in a parametrizable way is valuablefor a number of reasons, including privacy, missing data imputation, andevaluating the performance of statistical and computational methods. When theunderlying data generating process is complex, data synthesis requiresapproaches that balance realism and simplicity. In this paper, we address theproblem of synthesizing sequential categorical data of the type that isincreasingly available from mobile applications and sensors that recordparticipant status continuously over the course of multiple days and weeks. Wepropose the paired Markov Chain (paired-MC) method, a flexible framework thatproduces sequences that closely mimic real data while providing astraightforward mechanism for modifying characteristics of the synthesizedsequences. We demonstrate the paired-MC method on two datasets, one reflectingdaily human activity patterns collected via a smartphone application, and oneencoding the intensities of physical activity measured by wearableaccelerometers. In both settings, sequences synthesized by paired-MC bettercapture key characteristics of the real data than alternative approaches.

A flexible framework for synthesizing human activity patterns with application to sequential categorical data