Kriformer uses graph transformers to estimate data in sparse sensor areas.
problem Sparse sensor deployment and unreliable data in spatiotemporal kriging tasks.
method Graph transformer model with positional encoding and attention mechanisms.
result Kriformer excels in representing unobserved locations in spatiotemporal kriging tasks.
This paper reviews spatial and spatiotemporal volatility models.
problem Capturing spatial dependence in volatility of spatial and spatiotemporal data.
method Review of time series volatility models and their extensions.
result Comparison and practical recommendations for spatial and spatiotemporal volatility models.
Dynamic model captures spatial, temporal, and spatiotemporal volatility effects.
problem Analyzing volatility in spatial and temporal networks.
method Dynamic spatiotemporal and network ARCH model with common factors, Bayesian estimation.
result Model captures strong spatial/network interactions and spillover effects.
Bayesian Complementary Kernelized Learning models complex spatiotemporal data.
problem Modeling complex, nonstationary, and nonseparable spatiotemporal data.
method Integrates kernelized low-rank tensor factorization and short-range spatiotemporal Gaussian Processes.
result BCKL offers superior performance in providing accurate posterior mean and high-quality uncertainty estimates.
Package {mlr3spatiotempcv} simplifies spatiotemporal resampling methods in R.
problem Assessing and tuning spatial and spatiotemporal machine learning models.
method Integrates various spatiotemporal resampling methods into the {mlr3} framework.
result Provides a consistent interface for spatiotemporal resampling methods.
BKTR models spatiotemporal data with scalable tensor regression.
problem High computational cost in applying STVC to large-scale spatiotemporal data.
method Summarize STVC coefficients in a tensor, reformulate as low-rank tensor regression, incorporate GP priors for local dependencies.
result BKTR efficiently models large spatiotemporal datasets with reduced parameters and local dependencies.
GATGPT uses LLMs with graph attention for spatiotemporal data imputation.
problem Missing values in spatiotemporal data due to sensor malfunctions and data transmission errors.
method Integrates pre-trained large language models with graph attention mechanisms.
result GATGPT achieves comparable results to deep learning benchmarks on real-world datasets.
A new method for traffic data imputation considering spatiotemporal correlations.
problem Traffic data imputation, especially for high-level missing scenarios.
method Spatiotemporal regularized Tucker decomposition approach.
result The proposed method outperforms existing methods on real-world traffic datasets.
A new deep learning model improves robustness and efficiency in predicting continuous variables.
problem Limited applicability of deep learning in domains with small sample sizes.
method Autoencoder-based residual deep network with shortcut connections.
result Achieves cutting-edge accuracy and efficiency in multiple datasets.
Robust model detects outliers in spatiotemporal epidemic data.
problem Outliers in epidemic data can mislead public health decisions.
method RST-GAM with mean-shift, adaptive Lasso, splines, and proximal algorithm.
result Demonstrates effectiveness in real-world COVID-19 data analysis.
This paper studies uncertainty quantification in deep spatiotemporal forecasting.
problem Uncertainty quantification in deep spatiotemporal forecasting models.
method Analysis of UQ methods from Bayesian and frequentist perspectives, including statistical decision theory.
result Different UQ methods have different strengths and weaknesses, with Bayesian methods being more robust in mean prediction and frequentist methods providing more extensive coverage.
We propose a generic spatiotemporal event forecasting method, which we developed for the National Institute of Justice's (NIJ) Real-Time Crime Forecasting Challenge. Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian …
Deep models forecast epidemics with uncertainty quantification.
problem Accurate probabilistic forecasting of epidemics is challenging due to nonlinear temporal dependencies and spatial interactions.
method Deep spatiotemporal engression methods with geometric ergodicity and asymptotic stationarity.
result Proposed methods outperform benchmarks in point and probabilistic forecasting.
STVNN models spatiotemporal data using covariance matrices.
problem Challenges in modeling spatiotemporal interactions in multivariate time series.
method Introduces SpatioTemporal coVariance Neural Network (STVNN) that operates on sample covariance matrix and uses joint spatiotemporal convolutions.
result STVNN is stable to online estimation uncertainties and outperforms temporal PCA.
DeepRV accelerates spatiotemporal inference using neural priors.
problem Intractable scaling of Gaussian Processes for large datasets.
method Neural-network surrogate replacing GP prior sampling with O(N2) complexity. result DeepRV achieves highest fidelity to exact GPs while significantly speeding up inference.
Unified model forecasts epidemics with spatial and temporal dynamics.
problem Limited accuracy in traditional models and lack of interpretability in deep learning models.
method CSTGNN integrates Spatio-Contact SIR model with Graph Neural Networks.
result Effective spatiotemporal epidemic forecasting with interpretability.
Framework combines HMM and MTGCN for spatiotemporal causal inference in clinical data.
problem Challenges in observing direct treatment effects in clinical domains.
method Integrates Hidden Markov Model and Multi Task and Multi Graph Convolutional Network for spatiotemporal data.
result Advances predictive causal inference by structurally adapting to spatiotemporal complexities.
New method learns spatiotemporal dynamics from random point process observations.
problem Challenges in modeling spatiotemporal dynamics from randomly collected data.
method Integration of neural differential equations, neural point processes, implicit neural representations, and amortized variational inference.
result Significant improvements in predictive accuracy and computational efficiency compared to existing methods.
Chronnet models spatiotemporal data using chronological networks.
problem Handling large spatiotemporal datasets efficiently.
method Chronnet: Grid-based network model representing events chronologically.
result Chronnet captures frequent patterns, spatial changes, outliers, and clusters.
Paper uses DMD to embed time in spatiotemporal forecasting.
problem Forecasting long-range seasonal dependencies in spatiotemporal data.
method Dynamic Mode Decomposition (DMD) for time representation.
result DMD-based embedding improves long-horizon forecasting accuracy.
This paper surveys differential privacy methods for transportation spatiotemporal data.
problem Protecting user privacy in public release of spatiotemporal data.
method Review of differential privacy mechanisms and their application in transportation.
result Challenges in deploying and adopting differential privacy in transportation.
New unsupervised transfer learning method for spatiotemporal tasks.
problem Transfer knowledge from unsupervised models to new predictive tasks.
method Differentiable framework with Transferable Memory Unit (TMU).
result Significant improvements on spatiotemporal prediction benchmarks.
This paper proposes a geometry-aware active learning framework for spatiotemporal dynamic systems.
problem Challenges in modeling complex dynamic systems with 3D geometries and time evolution.
method Geometry-aware spatiotemporal Gaussian Process (G-ST-GP) and adaptive active learning strategy.
result The proposed framework outperforms traditional methods in predicting high-dimensional dynamic behaviors.
Model predicts urban population using mobile data traffic.
problem Estimating urban population dynamics from mobile data.
method Data-driven approach combining NetMob 2023 and ENACT datasets.
result NetMob 2023 data can estimate urban population with XGBoost models.
ProGen improves spatiotemporal forecasting with SDEs and diffusion models.
problem Complex spatial and temporal dependencies in spatiotemporal data.
method ProGen uses Stochastic Differential Equations and diffusion-based generative models.
result ProGen outperforms state-of-the-art models on traffic datasets.
A new method uses PDEs to predict spatiotemporal phenomena.
problem Predicting high-dimensional spatiotemporal data.
method Partial differential equations (PDEs) for spatiotemporal disentanglement.
result The method outperforms existing models in accuracy and applicability.
Casper uses causal graph neural networks to improve spatiotemporal time series imputation.
problem Imputing missing values in spatiotemporal time series with confounders and non-causal correlations.
method Casper introduces a novel Prompt Based Decoder (PBD) and Spatiotemporal Causal Attention (SCA) to block confounders and discover causal relationships.
result Casper outperforms baselines and effectively discovers causal relationships in spatiotemporal time series imputation.
New method improves tensor completion for weakly-dependent spatiotemporal data.
problem Improving tensor completion for weakly-dependent data on graphs.
method Introducing L1-norm and Graph Laplacian penalties for low-rank tensor decomposition and completion. result Improved performance in metro passenger flow prediction.
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…
Paper proposes a new model for imputing missing spatiotemporal traffic data.
problem Missing data and sparsity in spatiotemporal traffic data.
method Low-rank tensor completion (LRTC) framework with truncated nuclear norm (TNN).
result The proposed model outperforms state-of-the-art imputation models in various scenarios.
CBGP boosts GP covariance to model spatiotemporal irregularities.
problem Overfitting and overconfident uncertainty in nonstationary GP models.
method Boosting covariance priors, partially-whitened observations, gradient descent-like procedure.
result Accurate and reliable SBAS ionospheric corrections in challenging space weather.
MIM networks predict non-stationary spatiotemporal dynamics using differential signals.
problem Predicting non-stationary spatiotemporal processes with high-order variations.
method Memory In Memory (MIM) networks with cascaded memory modules.
result Achieved state-of-the-art results on four spatiotemporal prediction tasks.
This study improves weather forecasting accuracy with spatiotemporal models.
problem Complexity and resource-intensive nature of weather forecasting.
method Spatiotemporal forecasting models integrating machine learning and deep neural networks.
result Spatiotemporal models reduce computational costs and improve accuracy.
Enformer and GEnformer use Transformers with stochastic learning to forecast multivariate and spatiotemporal data with uncertainty.
problem Uncertainty quantification in multivariate time series and spatiotemporal forecasting.
method Synthesizing Transformer's expressive power with stochastic learning to model conditional distributions directly.
result Enformer and GEnformer yield calibrated probabilistic forecasts and outperform state-of-the-art baselines.
ESNs with transfer learning predict long-term chaotic patterns in spatiotemporal dynamical systems.
problem Predicting long-term statistical patterns of spatiotemporally chaotic dynamical systems.
method Echo state networks (ESNs) with transfer learning.
result ESNs with transfer learning accurately predict long-term statistical properties of spatiotemporally chaotic PDEs.
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
problem Challenges in detecting anomalies in spatiotemporal data, especially in urban traffic monitoring and medical imaging.
method Formulates anomaly detection as a regularized robust low-rank + sparse tensor decomposition, incorporating spatiotemporal smoothness and local dependencies.
result Demonstrates improved anomaly detection performance on both synthetic and real data.
New model fills in missing traffic data efficiently.
problem Missing data in large-scale spatiotemporal traffic data.
method Developed scalable tensor learning model LSTC-Tubal for imputation.
result LSTC-Tubal achieves high accuracy with lower computational cost.
New method reconstructs networks from spatiotemporal data.
problem Network reconstruction from spatiotemporal data.
method Multivariate Hawkes processes using both temporal and spatial information.
result Spatiotemporal approach yields improved network reconstruction.
Predicts financial asset dependencies using spatiotemporal patterns.
problem Complex dependency structures in financial assets for risk mitigation.
method Proposes Asset Dependency Matrix (ADM) and Asset Dependency Neural Network (ADNN) with ConvLSTM for spatiotemporal asset dependency prediction.
result ADNN outperforms baselines in predicting asset dependencies and their applications.
Framework uses physics knowledge to improve spatiotemporal prediction with limited data.
problem Challenges in modeling physical systems with limited real-world data.
method Physics-aware meta-learning with auxiliary tasks, incorporating PDE-independent spatial and temporal modules.
result Framework outperforms in spatiotemporal prediction tasks with limited data.
SoccerCPD detects tactical changes in soccer matches using spatiotemporal tracking data.
problem Detecting consistent team formations in fluid sports like soccer.
method Two-step change-point detection: formation and role changes.
result Accurately detects tactical changes and estimates formation and role assignments.
Geodesic curves improve flexibility in covariance estimation.
problem Inflexible covariance families limit spatiotemporal modeling.
method Use geodesic curves to build more flexible covariance families.
result Natural projection minimizes geodesic distance to sample covariance.
Spacetimeformer learns spatiotemporal relationships from data alone.
problem Forecasting multivariate time series with distinct spatial relationships.
method Transformers with dynamic graph connections learning interactions between space, time, and value.
result Competitive results on various time series prediction benchmarks.
SPACY discovers causal graphs from spatiotemporal data using variational inference.
problem Inferring causal relationships from high-dimensional spatiotemporal data with complex correlations.
method SPACY uses variational inference to model latent time series and their causal relationships, incorporating spatial factors to aggregate correlated data.
result SPACY outperforms state-of-the-art methods on synthetic and real-world data, identifying key causal phenomena.
Machine discovers PDEs from spatiotemporal data without prior knowledge.
problem Discovering PDEs from complex spatiotemporal data without prior knowledge.
method Sparse Spatiotemporal System Discovery (extS3extd) using Sparse Bayesian Learning. result Automatically discovers ten types of PDEs from simulation data.
CNN improves spatiotemporal emotion recognition from EEG during music listening.
problem Improving emotion recognition from EEG signals during music listening.
method Conducted a study on CNN and its spatiotemporal feature extraction for emotion recognition.
result CNN outperforms SVM in leave-one-subject-out cross validation.
CRRN detects printer defects in SPI data of SMT boards.
problem Detecting defects in solder paste inspection data.
method Convolutional Recurrent Reconstructive Network (CRRN) with CSTMs and ST-Attention.
result CRRN outperforms conventional models in anomaly detection.
USTAR combines multiple social media modalities to model user-guided activities.
problem Lack of comprehensive spatiotemporal activity models using all social media modalities.
method Online learning method embedding locations, time, text, and users into a single space, incorporating NGTSM and GTSM records, and using collaborative filtering.
result USTAR significantly improves region and keyword retrieval compared to state-of-the-art methods.