Related papers: Vector Autoregression (VAR) of Longitudinal Sleep …
Wearable devices offer detailed sleep-tracking data. However, whether this information enhances our understanding of sleep or simply quantifies already-known patterns remains unclear. This work explores the relationship between subjective…
Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences…
This paper develops a method for estimating parameters of a vector autoregression (VAR) observed in white noise. The estimation method assumes the noise variance matrix is known and does not require any iterative process. This study…
The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analyses. However, much research has reported the challenge of…
Complex systems, such as brains, markets, and societies, exhibit internal dynamics influenced by external factors. Disentangling delayed external effects from internal dynamics within these systems is often challenging. We propose using a…
Monitoring and understanding affective states are important aspects of healthy functioning and treatment of mood-based disorders. Recent advancements of ubiquitous wearable technologies have increased the reliability of such tools in…
The multiple-subject vector autoregression (multi-VAR) model captures heterogeneous network Granger causality across subjects by decomposing individual sparse VAR transition matrices into commonly shared and subject-unique paths. The model…
Users can easily export personal data from devices (e.g., weather station and fitness tracker) and services (e.g., screentime tracker and commits on GitHub) they use but struggle to gain valuable insights. To tackle this problem, we present…
Non-invasive mobile wearables like fitness trackers, smartwatches and rings allow for an easier and relatively less expensive approach to study everyday human behaviour when compared to traditional longitudinal methods. Here we have…
The vector autoregressive (VAR) model has been used to describe the dependence within and across multiple time series. This is a model for stationary time series which can be extended to allow the presence of a deterministic trend in each…
The vector autoregressive (VAR) model has been widely used for modeling temporal dependence in a multivariate time series. For large (and even moderate) dimensions, the number of AR coefficients can be prohibitively large, resulting in…
Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vector AutoRegressive (VAR) models, their popularity in…
Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human…
Vector autoregressive (VAR) models are widely used in practical studies, e.g., forecasting, modelling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this paper introduces a new…
Estimating personal well-being draws increasing attention particularly from healthcare and pharmaceutical industries. We propose an approach to estimate personal well-being in terms of various measurements such as anxiety, sleep quality and…
The attention for personalized mental health care is thriving. Research data specific to the individual, such as time series sensor data or data from intensive longitudinal studies, is relevant from a research perspective, as analyses on…
We study the problem of automatically discovering Granger causal relations from observational multivariate time-series data.Vector autoregressive (VAR) models have been time-tested for this problem, including Bayesian variants and more…
Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired…
Sleep is among the most important factors affecting one's daily performance, well-being, and life quality. Nevertheless, it became possible to measure it in daily life in an unobtrusive manner with wearable devices. Rather than camera…
Incorporating nonlinearity is paramount to predicting the future states of a dynamical system, its response to shocks, and its underlying causal network. However, most existing methods for causality detection and impulse response, such as…