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Found 14 papers, 3 papers with code
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones.
Identification of Craving Maps among Marijuana Users via the Analysis of Functional Brain Networks with High-Order Attention Graph Neural Networks
The excessive consumption of marijuana can induce substantial psychological and social consequences.
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual relationships.
Deep Attention Q-Network for Personalized Treatment Recommendation
Tailoring treatment for individual patients is crucial yet challenging in order to achieve optimal healthcare outcomes.
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
Many methods for PDE parameter inference involve a large number of evaluations for numerical solutions to PDE through algorithms such as the finite element method, which can be time-consuming, especially for nonlinear PDEs.
Estimating and Assessing Differential Equation Models with Time-Course Data
Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.
COVID-19 Hospitalizations Forecasts Using Internet Search Data
As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decision on medical resources allocations such as ICU beds, ventilators, and personnel to prepare for the surge of COVID-19 pandemics.
Order Book Queue Hawkes-Markovian Modeling
To capture the stimulating effects between multiple types of order book events, we use the multivariate Hawkes process to model the self- and mutually-exciting event arrivals.
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data
Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease.
MAGI-X: Manifold-Constrained Gaussian Process Inference for Unknown System Dynamics
Ordinary differential equations (ODEs), commonly used to characterize the dynamic systems, are difficult to propose in closed-form for many complicated scientific applications, even with the help of domain expert.
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction
The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has cast a significant threat to mankind.
Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes
MAGI uses a Gaussian process model over time-series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system.
Methodology Dynamical Systems
Use Internet Search Data to Accurately Track State-Level Influenza Epidemics
ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people's Internet search pattern.
Applications
Accurate estimation of influenza epidemics using Google search data via ARGO
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives.
