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.
Physics-informed methods infer spatial dynamics from static snapshots, but limits exist.
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The…
STCA discovers dynamic functional brain networks using spatial-temporal convolution and attention.
DISTANA predicts and denoises spatial wave dynamics.
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…
A new model predicts financial volatility across firms using spatial correlations.
Enhanced deep learning model forecasts household leverage series accurately.
TK-GCN forecasts spatiotemporal dynamics using Koopman-enhanced graph convolutional networks.
Extracts intrinsic spatial coordinates for complex agent systems to learn PDEs.
Nonlocal Bayesian modeling for continuous spatio-temporal dynamics
Dynamic models learn from sparse, interacting sub-systems.
The 2008 financial crisis revealed banking consolidation paradoxically increased systemic fragility and global financial contagion with negligible spatial decay.
Proposes a method to forecast spatial-temporal data with limited training data.
Deep learning model predicts traffic flows across entire network for multiple steps ahead.
Dynamic model captures spatial, temporal, and spatiotemporal volatility effects.
We propose a new class of models specifically tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series eco…
DINo forecasts PDEs with flexible extrapolation and adaptability.
Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or…
A new method identifies critical transitions in high-dimensional data.
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due to the lack of robust training algorithms. A bio-plausible SNN mode…
Epileptic seizure activity shows complicated dynamics in both space and time. To understand the evolution and propagation of seizures spatially extended sets of data need to be analysed. We have previously described an efficient filtering scheme using variational Laplace that can be used in the Dynamic Causal Modelling…
FLUID-LLM uses LLMs to predict fluid dynamics with improved accuracy.
DeepONets enhance spatial-temporal surrogates for structural dynamics.
Kernel Dynamic Mode Decomposition reconstructs dynamical systems using Laplacian kernel.
A3T-GCN improves traffic forecasting by capturing spatial and temporal dependencies.
A framework models order book dynamics using point processes and mass transport.
The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the si…
Using open source data, we observe the fascinating dynamics of nighttime light. Following a global economic regime shift, the planetary center of light can be seen moving eastwards at a pace of about 60 km per year. Introducing spatial light Gini coefficients, we find a universal pattern of human settlements across dif…
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data. GPs have been mainly applied to spatial regression where they represent one of the most powerful estimation approaches also thanks to their universal representing p…
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman…
Data-driven spatial filtering algorithms optimize scores such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and invol…
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different dom…
The moments of spatial probabilistic systems are often given by an infinite hierarchy of coupled differential equations. Moment closure methods are used to approximate a subset of low order moments by terminating the hierarchy at some order and replacing higher order terms with functions of lower order ones. For a give…
Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively smooth and possibly nonlinear. We present Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is abl…
DMSTF models spatio-temporal data with deep Markov priors.
Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic Origin-Destination (O-D) demand matrices is that demands cannot be directly measured by traffic sensors; instead, they have t…
Recent research in economic theory attempts to study optimal economic growth and spatial location of economic activity in a unified framework. So far, the key result of this literature - asymptotic convergence, even in the absence of decreasing returns to capital - relies on specific assumptions about the objective of …
Spatial ABM predicts housing market trends in Sydney.
MIP framework improves urban flow prediction by adapting to distribution shifts.
Novel spatio-temporal LSTM model forecasts oceanic variables across sensors and scales.
This article is motivated by soccer positional passing networks collected across multiple games. We refer to these data as replicated spatial passing networks---to accurately model such data it is necessary to take into account the spatial positions of the passer and receiver for each passing event. This spatial regist…
BrainCast predicts whole-brain fMRI time series from short scans.
Diffusion Transformer captures spatial-temporal dependencies in sequential data.
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
New method to classify simple Smale flows on .
Proposes a new model for complex multivariate event data.
Decentralized framework for spatial data inference over vulnerabilities.