Combines CNN and LSTM for spatio-temporal graph networks.
problem Improving spatio-temporal feature extraction.
method Proposes a new architecture combining CNN and LSTM temporal blocks.
result Empirical comparison shows our model outperforms existing models.
Localized CNNs improve geospatial wind forecasting.
problem Improving CNN performance in geospatial, spatio-temporal prediction.
method Localized convolutional neural networks (LCNNs) that learn local features in addition to global ones.
result LCNNs enhance wind forecasting models, often surpassing state-of-the-art.
Deep CNN models improve spatio-temporal forecasting efficiency.
problem Efficiently forecasting spatio-temporal dynamics with realistic models.
method Hierarchical statistical IDE framework with CNN for dynamic extraction.
result CNN provides accurate, interpretable, and computationally efficient forecasts.
DeepIST uses CNNs to estimate travel time from path images.
problem Accurately estimating travel time for a path in urban transportation systems.
method Proposes DeepIST, a neural network framework that converts paths into generalized images and uses PathCNN and 1D CNN to capture spatial and temporal patterns.
result DeepIST significantly outperforms existing models in travel time estimation.
Deep learning models improve spatio-temporal data mining.
problem Mining valuable knowledge from spatio-temporal data.
method Application of deep learning techniques (CNN, RNN) in various spatio-temporal data mining tasks.
result Deep learning models enhance performance in spatio-temporal data mining.
A CNN method removes atmospheric distortion from video sequences.
problem Mitigating atmospheric distortion in video sequences.
method End-to-end supervised convolutional neural network (CNN) with residual learning.
result The method outperforms existing techniques in terms of image quality and speed.
End-to-end CNN for real-time MOD improves KITTI dataset accuracy by 8%.
problem Real-time detection of moving objects for autonomous vehicles.
method Spatio-temporal context and time-aware architecture using optical flow.
result Improvement of 8% in KITTI dataset accuracy compared to baselines.
Simple model predicts trajectory probabilities.
problem Forecasting trajectories from visual data.
method Spatio-temporal convolutional neural network.
result Achieves results on par with or better than existing methods.
Two autoencoding models learn latent traffic scene representations.
problem Learning latent representations of traffic scenarios.
method CNN and RNN models for spatio-temporal and temporal data, incorporating permutation invariance.
result Latent scenario embeddings can be used for clustering and similarity retrieval.
Paper models spatio-temporal extremes using conditional variational autoencoders.
problem Modeling co-occurrence of extreme weather events under changing climate conditions.
method Conditional Variational Autoencoder (cXVAE) with CNN integration.
result Accurately emulates spatial fields and recovers extremal dependence with low computational cost.
Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems and semantic crawlers of la…
Unified tensor factorization for efficient 3D convolutions in spatio-temporal emotion analysis.
problem Training deep 3D convolutions is computationally expensive and requires large datasets.
method Tensor factorization framework for separable higher-order convolutions.
result Improved spatio-temporal emotion estimation on large datasets.
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,…
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
problem Limited transferability of deep learning models for traffic flow prediction across different locations.
method Integrates Newell's traffic flow estimators to capture broader dynamics and incorporates spatial dependencies.
result Improves model performance in predicting traffic flows over different horizons.
Advanced travel information and warning, if provided accurately, can help road users avoid traffic congestion through dynamic route planning and behavior change. It also enables traffic control centres mitigate the impact of congestion by activating Intelligent Transport System (ITS) proactively. Deep learning has beco…
Novel hybrid bilinear model improves epilepsy diagnosis accuracy.
problem Improving accuracy in epilepsy diagnosis and treatment.
method Hybrid bilinear deep learning network using sEEG and audiovisual monitoring.
result Obtained F1-scores of 97.4% and 97.2% on two seizure datasets.
TSCoNet forecasts correlated geophysical fields with uncertainty estimates.
problem Accurate and reliable forecasts of correlated geophysical fields across many locations.
method Two-stage CNN-LSTM coupled with Gaussian copula.
result Calibrated prediction intervals without sacrificing point accuracy.
Deep learning model predicts wind-wave relationship.
problem Characterize ocean wave climate for engineering applications.
method Two-stage deep learning model: CNN for spatial features, LSTM for temporal dependencies.
result Predicts spatio-temporal relationship between wind and significant wave height.
STACI uses neural nets to estimate spatio-temporal fields with valid uncertainty quantification.
problem Scalable spatio-temporal deep learning models fail to capture underlying correlation structure.
method Variational Bayesian neural network approximation of non-stationary spatio-temporal Gaussian Process (GP) with conformal inference.
result STACI provides accurate prediction intervals for spatio-temporal processes, outperforming competing methods.
Robust spatio-temporal GP framework for outlier-resilient predictions.
problem Outliers in spatio-temporal data degrade GP performance.
method Adapted and specialised RCGP framework for spatio-temporal settings.
result RCGP provides reliable spatio-temporal predictions with outliers.
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural network…
FreST Loss decorrelates spatio-temporal dependencies in graph signals.
problem Complex spatio-temporal dependencies in graph-structured signals are not well captured by standard forecasting models.
method FreST Loss extends supervision to the joint spatio-temporal spectrum using Joint Fourier Transform (JFT).
result FreST Loss reduces estimation bias and improves forecasting accuracy on real-world datasets.
New framework predicts urban traffic with high accuracy.
problem Urban traffic prediction challenges.
method Interpretable attention-based neural network combining multiple modules.
result Framework outperforms state-of-the-art alternatives.
Deep learning improves PS pixel selection in SAR interferometry.
problem Selecting persistent scatterer pixels for geophysical parameter estimation in multi-temporal SAR interferometry.
method Proposed two deep learning architectures: CNN-ISS and CLSTM-ISS trained on phase history to classify PS and non-PS pixels.
result CLSTM-ISS outperforms conventional methods in PS pixel selection and classification accuracy.
Convolutional LSTM improves missing data imputation in spatio-temporal data.
problem Missing data in spatio-temporal data affects analysis performance.
method Proposes a convolutional bidirectional-LSTM for spatio-temporal missing data imputation.
result The proposed model outperforms state-of-the-art methods for missing data imputation.
A framework uses deep learning for spatio-temporal data prediction.
problem Interpolation of continuous spatio-temporal fields on irregular points.
method Decomposes spatio-temporal processes into products of basis functions and spatial coefficients.
result Effectiveness in reconstructing coherent spatio-temporal fields.
This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in v…
Proposes a new model for complex multivariate event data.
problem Modeling complex multivariate event data with spatio-temporal dynamics.
method Integrates spatial information into latent state evolution through learned temporal and spatial decay dynamics.
result Successfully recovers sensible temporal and spatial intensity structure in multivariate spatio-temporal point patterns.
New graph kernels capture spatio-temporal interactions.
problem Lack of justified spatio-temporal graph kernels for graph problems.
method Derive graph kernels via SPDEs for spatio-temporal modelling.
result Non-separable spatio-temporal graph kernels outperform existing ones.
New neural networks model for spatio-temporal data.
problem Building a mapping from spatially encoded time series covariates to real-valued response data.
method Proposed two novel extensions of Functional Neural Network (FNN) for spatio-temporal regression.
result Demonstrated effectiveness in handling varying spatial correlations through comprehensive simulation studies.
ST-UNet models spatio-temporal graphs by pooling and unpooling operations.
problem Lack of effective means to extract dynamic features from spatio-temporal graphs.
method Designing a multi-scale architecture, Spatio-Temporal U-Net (ST-UNet), with paired sampling operations.
result Achieves substantial improvements in spatio-temporal prediction tasks.
New method predicts spatio-temporal data with short and long-range dependence.
problem Uncertainty in predicting the distribution of mixed moving average fields.
method Theory-guided machine learning approach using generalized Bayesian algorithm.
result Fixed-time and any-time PAC Bayesian bounds for ensemble forecasts.
New model infers causal relationships from spatio-temporal data, even with unobserved confounders.
problem Challenges in inferring causal relationships from spatio-temporal data due to unobserved confounders.
method Spatio-Temporal Hierarchical Causal Models (ST-HCMs) that extend hierarchical causal modeling to the spatio-temporal domain, using the Spatio-Temporal Collapse Theorem.
result Validated the effectiveness of ST-HCMs on both synthetic and real-world datasets, demonstrating robust causal inference in complex dynamic systems.
FNOs improve spatio-temporal forecasting without needing PDE details.
problem Complex spatio-temporal dynamics in physical and biological phenomena.
method Fourier Neural Operators (FNOs) for dynamic spatio-temporal modeling.
result FNO forecasts are accurate and capture complex real-world dependencies.
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
problem Improving accuracy of default probabilities and loan portfolio loss distributions in mortgage credit risk.
method Combines tree-boosting with a latent spatio-temporal Gaussian process model.
result Predictive models outperform conventional methods due to non-linear and spatio-temporal effects.
STDGI learns node representations for spatio-temporal graphs via mutual information maximization.
problem Challenges in learning node representations for spatio-temporal graphs due to structural changes over time.
method STDGI is a fully unsupervised approach based on mutual information maximization that exploits both spatial and temporal dynamics.
result STDGI's learned node representations improve spatio-temporal auto-regressive forecasting models.
Mixed DNN approach improves EEG-based speech imagery recognition.
problem Automatic identification of imagined speech from EEG.
method Hierarchical deep neural network strategy combining CNN, RNN, and autoencoders.
result 23.45% improvement in accuracy over baseline method.
Spatio-temporal RBF neural networks improve chaotic time series prediction.
problem Predicting chaotic time series due to their dynamic nature.
method Proposes an spatio-temporal extension of RBF neural networks.
result Spatio-temporal RBF outperforms standard RBF in chaotic time series prediction.
Modeling solar ramping events with spatio-temporal point processes.
problem Predicting solar ramping events influenced by weather conditions.
method Novel spatio-temporal categorical point process model.
result Effective modeling of spatio-temporal correlations in solar ramping events.
A new kernel framework analyzes spatio-temporal data from dynamic equations.
problem Analyzing spatio-temporal data from dynamic equations with noisy measurements.
method Kernel-based framework with representer theorem for minimizing error with given samples.
result Minimizes error in solutions of dynamic equations with noisy spatio-temporal data.
Deep learning predicts traffic flow on Sydney motorways.
problem Challenging traffic flow prediction due to inter-dependencies.
method Advanced deep learning framework using CNN-LSTM.
result Deep learning models outperform traditional methods.
Proposes FairDRL-ST for fair spatio-temporal mobility prediction.
problem Fairness concerns in spatio-temporal AI applications.
method Disentangled representation learning, adversarial learning.
result Achieves fairness in spatio-temporal mobility prediction without performance loss.
This paper tackles spatio-temporal information preservation in machine learning.
problem Conventional machine learning assumes orthogonal data attributes, disrupting spatio-temporal information.
method Shift-invariant k-means, convolutional dictionary learning, and spatio-temporal hypercomplex encoding schemes are proposed.
result Gabor feature extraction outperforms convolutional dictionary learning in spatio-temporal information preservation.
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 …
Dynamic sample pruning speeds up spatio-temporal forecasting models.
problem Training deep learning models on large, redundant datasets is computationally expensive.
method Dynamic sample pruning based on real-time learning state.
result Significant acceleration of training speed with improved performance.
3D-TGCN learns road graphs from time series similarity for spatio-temporal traffic forecasting.
problem Challenging spatio-temporal prediction in traffic networks due to dependency and dynamics.
method Proposes 3D-TGCN with novel components: spatial information-free road graph and 3D graph convolution.
result 3D-TGCN outperforms state-of-the-art baselines in traffic forecasting.
DeepKriging uses neural networks for spatio-temporal interpolation and forecasting.
problem Non-Gaussianity and nonstationarity in real-world data.
method Two-stage model: DNN for interpolation, LSTM for forecasting.
result DeepKriging provides probabilistic forecasts without stationarity assumptions.
Improved RBFNN for nonlinear system identification.
problem Nonlinear system identification problem.
method Spatio-temporal extension of RBFNN with time-space orthogonality.
result Spatio-temporal RBFNN outperforms standard RBFNN in convergence and error reduction.