  We consider the problem of predicting an individual's identity from
accelerometry data collected during walking. In a previous paper we introduced
an approach that transforms the accelerometry time series into an image by
constructing its complete empirical autocorrelation distribution. Predictors
derived by partitioning this image into grid cells were used in logistic
regression to predict individuals. Here we: (1) implement machine learning
methods for prediction using the grid cell-derived predictors; (2) derive
inferential methods to screen for the most predictive grid cells; and (3)
develop a novel multivariate functional regression model that avoids
partitioning of the predictor space into cells. Prediction methods are compared
on two open source data sets: (1) accelerometry data collected from $32$
individuals walking on a $1.06$ kilometer path; and (2) accelerometry data
collected from six repetitions of walking on a $20$ meter path on two separate
occasions at least one week apart for $153$ study participants. In the
$32$-individual study, all methods achieve at least $95$% rank-1 accuracy,
while in the $153$-individual study, accuracy varies from $41$% to $98$%,
depending on the method and prediction task. Methods provide insights into why
some individuals are easier to predict than others.
