  Increased use of sensor signals from wearable devices as rich sources of
physiological data has sparked growing interest in developing health monitoring
systems to identify changes in an individual's health profile. Indeed, machine
learning models for sensor signals have enabled a diverse range of healthcare
related applications including early detection of abnormalities, fertility
tracking, and adverse drug effect prediction. However, these models can fail to
account for the dependent high-dimensional nature of the underlying sensor
signals. In this paper, we introduce Latent Temporal Flows, a method for
multivariate time-series modeling tailored to this setting. We assume that a
set of sequences is generated from a multivariate probabilistic model of an
unobserved time-varying low-dimensional latent vector. Latent Temporal Flows
simultaneously recovers a transformation of the observed sequences into
lower-dimensional latent representations via deep autoencoder mappings, and
estimates a temporally-conditioned probabilistic model via normalizing flows.
Using data from the Apple Heart and Movement Study (AH&MS), we illustrate
promising forecasting performance on these challenging signals. Additionally,
by analyzing two and three dimensional representations learned by our model, we
show that we can identify participants' $\text{VO}_2\text{max}$, a main
indicator and summary of cardio-respiratory fitness, using only lower-level
signals. Finally, we show that the proposed method consistently outperforms the
state-of-the-art in multi-step forecasting benchmarks (achieving at least a
$10\%$ performance improvement) on several real-world datasets, while enjoying
increased computational efficiency.
