A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimized DMD for fast atmospheric chemistry forecasting.
Nonlocal Bayesian modeling for continuous spatio-temporal dynamics
Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both space and time, this structure can be exploited by splitting the global field into many lower-dimensional "light cones". We review light cone …
The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is s…
Spatio-temporal data compression method reduces memory usage.
A new kernel framework analyzes spatio-temporal data from dynamic equations.
Dynamic sample pruning speeds up spatio-temporal forecasting models.
FNOs improve spatio-temporal forecasting without needing PDE details.
New method clusters evolving networks using spatio-temporal graph Laplacian.
New model tackles complex spatio-temporal causal inference with dynamic confounders and functional data.
Proposes a new model for complex multivariate event data.
In recent years, Generative Adversarial Networks (GAN) have emerged as a powerful method for learning the mapping from noisy latent spaces to realistic data samples in high-dimensional space. So far, the development and application of GANs have been predominantly focused on spatial data such as images. In this project,…
New model infers causal relationships from spatio-temporal data, even with unobserved confounders.
Novel framework for spatio-temporal event analysis using Hawkes processes.
Whole MILC learns brain disorder dynamics from unlabeled data.
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingl…
Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both …
PhICNet combines physics and deep learning for forecasting and source identification in dynamical systems.
Analyzing mobility behavior of users is extremely useful to create or improve existing services. Several research works have been done in order to study mobility behavior of users that mainly use users' significant locations. However, these existing analysis are extremely intrusive because they require the knowledge of…
DeepONets enhance spatial-temporal surrogates for structural dynamics.
FreST Loss decorrelates spatio-temporal dependencies in graph signals.
GNNs improve brain activity forecasting in fMRI studies.
Modeling wind dynamics in Saudi Arabia using deep learning and stochastic PDEs.
A new framework for averaging spatio-temporal signals using optimal transport and soft alignments.
DMSTF models spatio-temporal data with deep Markov priors.
Novel Bayesian framework for spatio-temporal neuroimaging data.
Paper presents a spatio-temporal Bayesian model for early detection of COVID-19 hotspots.
Herein, we propose a spatio-temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. The proposed RBF architecture is explored for the estimation of a highl…
DANR improves network regularization for spatio-temporal data.
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
In the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible. The availability of such rich and detailed physiological measurements calls for the development of advanced data analysis tools, as commonly used techniques do …
ST-GCN improves rs-fMRI prediction accuracy by modeling spatio-temporal graph connectivity.
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time. To address these issues, we introduce Spatio-Temporal Deep Graph Infomax (STDGI)---a fully unsupervised node representation learning…
Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines. These processes are often characterized by nonlinear time dynamics that include interactions across multiple scales of spatial and temporal variability. The data sets associated with many of these processes are increasing …
Long-lead forecasting for spatio-temporal systems can often entail complex nonlinear dynamics that are difficult to specify it a priori. Current statistical methodologies for modeling these processes are often highly parameterized and thus, challenging to implement from a computational perspective. One potential parsim…
Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatio-temporal information. Although pre-training from ANN or direct training based on backpropagation (BP) makes the supervised training of SNNs possible…
A new test detects noise in graph data, useful for forecasting.
New method detects and locates changes in spatio-temporal point processes.
Framework reconstructs missing spatio-temporal data for extreme value prediction.
Quantitative modeling of post-transcriptional regulation process is a challenging problem in systems biology. A mechanical model of the regulatory process needs to be able to describe the available spatio-temporal protein concentration and mRNA expression data and recover the continuous spatio-temporal fields. Rigorous…
Spatio-temporal prediction plays an important role in many application areas especially in traffic domain. However, due to complicated spatio-temporal dependency and high non-linear dynamics in road networks, traffic prediction task is still challenging. Existing works either exhibit heavy training cost or fail to accu…
Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of p…
Improved Gaussian process inference for spatio-temporal data.
With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real world applications including huma…
Model predicts terror attacks' likelihood and casualties.
Deep learning speeds up real-time emission monitoring.
In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian Conditional …
We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for forecasting time series of spatial processes, i.e. series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts t…