Graph WaveNet models spatial-temporal graphs by learning hidden dependencies and long sequences.
problem Capturing hidden spatial dependencies and long-range temporal sequences in graphs.
method Graph WaveNet integrates adaptive dependency matrix learning and stacked dilated 1D convolution.
result Graph WaveNet outperforms existing methods on public traffic network datasets.
STOIC improves energy demand forecasting with reliable uncertainty estimates.
problem Accurate point forecasts alone are insufficient for energy systems; reliable uncertainty estimates are needed.
method Integrates graph-based forecasting with tabular foundation models for zero-shot calibration of spatial-temporal residuals.
result STOIC delivers more reliable and robust uncertainty estimates for complex graph-structured energy time series.
Survey of urban flows prediction methods using various datasets.
problem Predicting urban flows influenced by human activities, weather, events, and holidays.
method Analysis of four main factors, preparation of multi-sources spatial-temporal data, detailed comparison of five categories of prediction methods.
result Facilitates researchers to choose suitable methods and datasets for urban flows prediction.
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…
Proposes a method to forecast spatial-temporal data with limited training data.
problem Forecasting with nodes having no temporal training data.
method Temporal data augmentation and spatial graph topology learning.
result Improves forecasting performance on nodes without training data.
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…
Diffusion Transformer captures spatial-temporal dependencies in sequential data.
problem Capturing rich spatial and temporal dependencies in sequential data.
method Established theoretical guarantees for diffusion transformers learning Gaussian process data.
result Spatial-temporal dependencies are captured within attention layers of diffusion transformers.
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality rel…
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities ma…
Proposes a GNN for multivariate time-series prediction with filtering.
problem Low signal-to-noise ratio in complex systems data.
method Integrates a spatial-temporal GNN with a matrix filtering module to generate filtered graphs.
result Proposed model outperforms baseline approaches in multivariate time-series prediction.
New model predicts travel demand uncertainty with high accuracy.
problem Uncertainty and sparsity in sparse travel demand prediction.
method Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN).
result STZINB-GNN outperforms benchmarks in predicting travel demand uncertainty.
MIP framework improves urban flow prediction by adapting to distribution shifts.
problem Distribution shifts in urban flow data make prediction models unreliable.
method Memory-enhanced Invariant Prompt learning with learnable memory bank.
result MIP ensures robust predictions by focusing on invariant features.
Deep-MAPS uses machine learning for mobile air pollution sensing in Beijing.
problem Ubiquitous sensing of urban air quality.
method Machine learning framework (Deep-MAPS) based on mobile and fixed sensors.
result Deep-MAPS achieves high spatial-temporal resolution (1km-by-1km and 1 hour) with over 85% accuracy.
New training algorithm enhances SNNs for temporal signal processing.
problem Lack of robust training algorithms for large-scale SNNs.
method Formulated SNN as IIR filters, proposed training algorithm for optimal synapse filter kernels and weights.
result Model and training algorithm outperform state-of-the-art approaches in accuracy.
STCA discovers dynamic functional brain networks using spatial-temporal convolution and attention.
problem Lack of dynamic exploration of functional brain networks.
method Spatial-Temporal Convolutional Attention (STCA) model.
result STCA can discover dynamic functional brain networks in a novel way.
MAOP learns object dynamics from raw visual data.
problem Efficient learning of dynamics from raw visual data for multiple objects.
method Three-level learning architecture with spatial-temporal relational reasoning.
result Significantly outperforms previous methods in sample efficiency and generalization.
Proposes a non-autoregressive Transformer for time series forecasting.
problem Autoregressive errors and spatial-temporal dependencies in time series forecasting.
method Introduces a Non-Autoregressive Transformer with a learned temporal influence map.
result Demonstrates state-of-the-art performance on time series forecasting datasets.
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing…
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and…
A new method detects financial fraud using graph transformers.
problem Detecting fraudulent transactions in financial data.
method Spatial-Temporal-Aware Graph Transformer (STA-GT) integrating GNNs and transformers.
result STA-GT outperforms general GNN models on financial fraud detection.
Proposes KStar Diffuser for kinematics-aware bimanual robotic manipulation.
problem Challenges in applying imitation learning to bimanual robotic tasks.
method Integrates physical robot structure into action prediction using a dynamic spatial-temporal graph and differentiable kinematics.
result Effective generation of kinematics-aware actions in both simulation and real-world environments.
ST-SAN predicts flow with spatial-temporal dependencies using self-attention.
problem Challenges in predicting flow due to spatial-temporal dependencies.
method Spatial-Temporal Self-Attention Network (ST-SAN) that addresses temporal and spatial dependencies.
result Significant improvement in flow prediction accuracy (9% in inflow, 4% in outflow) compared to state-of-the-art methods.
STG2Seq predicts multi-step passenger demand with graph and hierarchical structure.
problem Predicting passenger demand over multiple time horizons is challenging due to nonlinear and dynamic spatial-temporal dependencies.
method Proposes a graph-based model with a hierarchical graph convolutional structure to capture spatial and temporal correlations.
result Consistently outperforms baseline and state-of-the-art models on real-world datasets.
Proposes a new model to predict travel demand with zero-inflated and long-tail characteristics.
problem Sparse and long-tailed travel demand data with many zeros.
method Spatial-Temporal Tweedie Graph Neural Network (STTD) using Tweedie distribution.
result STTD provides accurate predictions and precise confidence intervals.
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
problem Relapse prevention for alcohol and tobacco addiction users.
method Records user profiles, tracks relapse history, uses machine learning for prediction, and recommends activities.
result Predictive machine learning algorithms help in preventing relapse.
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.
ATFM predicts traffic flow using attention mechanism and neural networks.
problem Predicting traffic flow with diverse factors integration.
method Unified neural network (ATFM) with attention mechanism and ConvLSTM units.
result ATFM outperforms in predicting citywide short-term/long-term traffic flow.
New BNN method reduces training time and model size.
problem Overconfident predictions in deep learning models.
method Designing STF-BNN for efficient scaling of BNNs.
result Significantly reduces training time and model size compared to vanilla BNNs.
Paper improves generalization bounds for multi-kernel learning with mixed datasets.
problem Improving generalization for multi-kernel learning with mixed Markov chain datasets.
method Developed novel generalization bounds with O ( log m ) O(\sqrt{\log m}) O ( log m ) and O ( 1 / n ) O(1/\sqrt{n}) O ( 1/ n ) dependencies. result Added terms compensate for dependency among samples in mixed datasets.
DeepONets enhance spatial-temporal surrogates for structural dynamics.
problem Creating full spatial-temporal surrogates for dynamical systems under uncertainty.
method Proposed Full-Field Extended DeepONet (FExD) to learn full solution operator across multiple degrees of freedom.
result FExD achieves superior accuracy and computational efficiency compared to other models.
Finite element method applied to Leland's model for option pricing with transaction costs.
problem Option pricing with transaction costs using Leland's model.
method Spatial finite element models based on P1 and/or P2 elements combined with a Crank-Nicolson-type temporal scheme.
result Results compare favorably with finite difference methods in the literature.
Study compares deep learning models for traffic forecasting, highlighting graph elements' impact.
problem Challenges in forecasting spatial-temporal traffic patterns.
method In-depth comparative study of four deep neural network models with different basic elements.
result Graph attention improves long-term predictions in traffic forecasting models.
We propose two methods for exact Gaussian process (GP) inference and learning on massive image, video, spatial-temporal, or multi-output datasets with missing values (or "gaps") in the observed responses. The first method ignores the gaps using sparse selection matrices and a highly effective low-rank preconditioner is…
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
problem Predicting future ticket prices from sparse and spatiotemporal data.
method Bi-level optimizing deep neural network with coarsening and refining layers, bi-level loss function.
result Our model outperforms other methods in real-world ticket price prediction.
Kernel Dynamic Mode Decomposition reconstructs dynamical systems using Laplacian kernel.
problem Reconstructing spatial-temporal dynamics of complex systems.
method Kernel Dynamic Mode Decomposition with Laplacian kernel.
result Laplacian kernel allows for the closability of Koopman operators in RKHS, enabling reconstruction.
Hybrid model predicts flow and pressure in water systems.
problem Predicting flow and pressure in water distribution systems with complex spatial-temporal correlations.
method Hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN).
result Our model outperformed 9 baseline models in flow and pressure series prediction.
kNN-MTS improves MTS forecasting by using nearest neighbor retrieval over a large dataset.
problem Limited ability of current MTS forecasting methods to identify similar patterns and handle sparsely distributed correlations.
method kNN-MTS framework using nearest neighbor retrieval over a large datastore of cached series, with representations from MTS model for similarity search.
result Significant improvement in forecasting performance on real-world datasets.
Paper proposes a neural network to improve traffic flow forecasting.
problem Forecasting future traffic flow distribution in an area.
method Position-aware convolutional neural network integrating data features and position information.
result Our approach outperforms previous methods even with fewer data sources.
Satellite imagery helps assess sustainable development with machine learning.
problem Lack of ground data on sustainable development outcomes.
method Combining satellite imagery with machine learning to model outcomes.
result Machine learning models perform well across multiple sustainable development domains.
CSTN predicts taxi demand between all regions, overcoming origin-only approaches.
problem Predicting taxi demand between all regions, not just origins.
method Contextualized Spatial-Temporal Network (CSTN) with LSC, TEC, and GCC modules.
result CSTN outperforms other methods in taxi origin-destination demand prediction.
Novel Bayesian model improves EEG-based BCI character selection.
problem Accurately identifying target-related responses in EEG-based BCIs.
method Probit-link Split-and-merge Gaussian Process (P-SMGP) prior for feature selection.
result Reduces computational complexity and provides interpretable statistical interpretations.
Novel BCI system classifies imagined speech with high accuracy.
problem Classifying imagined speech from brain signals.
method Hierarchical deep learning with CNN and autoencoder.
result Achieved 83.42% average accuracy across six phonological tasks.
Two prediction models improve supply-demand forecasting for autonomous vehicles.
problem Improving accuracy and stability of supply-demand predictions for autonomous vehicles.
method Two prediction models based on residual network, LSTM, attention mechanism, and multi-attention mechanism.
result Our frameworks provide more accurate and stable prediction results than existing methods.
A new method identifies critical transitions in high-dimensional data.
problem Challenges in identifying critical transitions in high-dimensional time-series data.
method Spatial-temporal Principal Component Analysis (stPCA)
result Identifies tipping points before critical transitions reliably.
New method uses neural networks to forecast spatial-temporal data.
problem Probabilistic forecasting of spatio-temporal data with causal structure.
method MMAF-guided learning with ensemble of stochastic feed-forward neural networks.
result Forecasting remains calibrated across multiple time horizons.
A new framework combines CNN and GRU for better structural damage detection.
problem Improving damage detection in structural engineering using machine learning.
method Hierarchical CNN and Gated Recurrent Unit (GRU) framework to model spatial and temporal relations.
result The proposed HCG framework significantly outperforms existing methods for structural damage detection.
MRA-BGCN improves traffic forecasting accuracy through complex graph interactions.
problem Challenging traffic forecasting due to spatial-temporal dependency and uncertainty.
method Proposes MRA-BGCN, a deep learning model that uses bicomponent graph convolution and multi-range attention.
result MRA-BGCN achieves state-of-the-art results on real-world traffic datasets.
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or unsupervised learning…