New method clusters evolving networks using spatio-temporal graph Laplacian.
problem Clustering communities in time-varying graphs.
method Extends spectral clustering to dynamic graphs using CCA and spatio-temporal graph Laplacian.
result The spatio-temporal graph Laplacian clearly interprets cluster evolution over time.
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.
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.
New framework assesses deep learning models for spatio-temporal data with missing data.
problem Challenges in assessing deep learning models for spatio-temporal data with missing and heterogeneous data.
method Residual correlation analysis framework using spatio-temporal graphs and asymptotically distribution-free summary statistics.
result Identification and localization of regions where predictive performance can be improved.
New neural network predicts traffic flow across different cities.
problem Forecasting traffic flow across different cities is challenging due to spatio-temporal correlations.
method Proposes a local-spacetime neural network (STNN) that captures universal spatio-temporal correlations.
result Improves prediction accuracy by 4% over state-of-the-art methods.
A simple baseline outperforms deep learning methods in transportation forecasting.
problem The importance of stationarity and recurrent patterns in transportation data.
method A naive baseline based on average weekly patterns and linear regression.
result The baseline method achieves comparable or better results than state-of-the-art deep learning approaches.
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.
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.
Paper proposes a GAN-based approach for RTLMP prediction.
problem Predicting real-time locational marginal prices (RTLMPs) in power markets.
method GAN-based video prediction model for spatio-temporal correlations.
result Proposed method accurately predicts RTLMPs without confidential information.
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…
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.
Spatio-temporal data compression method reduces memory usage.
problem Efficiently storing and analyzing large spatio-temporal datasets.
method Adaptive sampling of tensor slices to compress and preserve structure.
result SkeTenSmooth outperforms other sampling methods in retaining patterns.
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a nove…
Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation.However, existing methods still perform poorly on challenging video tasks such as long-term forecasting. This is because these kinds of challenging tasks require learn…
Spatial information is not always necessary for spatio-temporal models.
problem The necessity of including spatial information in spatio-temporal models.
method Comparison of spatial agnostic neural networks with state-of-the-art models on ten datasets.
result Spatial information is not always needed in most spatio-temporal models.
This study predicts parking availability using multi-source data and a self-supervised learning enhanced transformer.
problem Accurate parking availability prediction to support urban planning and management.
method Proposes SST-iTransformer, a self-supervised learning enhanced spatio-temporal inverted transformer, integrating multi-source data.
result SST-iTransformer achieves state-of-the-art performance in parking availability prediction.
We propose a neural network based approach for extracting models from dynamic data using ordinary and partial differential equations. In particular, given a time-series or spatio-temporal dataset, we seek to identify an accurate governing system which respects the intrinsic differential structure. The unknown governing…
The paper uses deep learning to detect financial market regimes from correlation matrices.
problem Detecting financial market regimes from correlation dynamics.
method Representation learning on block hierarchical SPD correlation matrices using SPDNet, SPD-NetBN, and U-SPDNet models.
result Deep learning models overfit in financial market data, misleading performance metrics.
Challenge forecasts EV charging station usage accurately.
problem Better understanding of EV charging behaviors needed.
method Hierarchical forecasting of EV charging station occupancy.
result Hierarchical forecasting improves EV charging station occupancy predictions.
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.
Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term dependence of the temporal relationships between different series.…
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.
TCGPN improves stock forecasting by capturing temporal correlation patterns.
problem Stock forecasting with minimal periodicity and large node numbers.
method TCGPN uses Temporal-Correlation fusion encoder and pre-training methods to handle large datasets.
result TCGPN achieves state-of-the-art results on real stock market data.
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.
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.
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.
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study…
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.
Deviance Voronoi residuals improve earthquake insurance risk assessment.
problem Assessing earthquake insurance risk using spatio-temporal point process models.
method Extended Voronoi residuals and created simulation-based approach.
result Proposed formula for country-wide minimum capital test.
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.
New method improves traffic data recovery for streaming data.
problem Improve data quality in traffic data for ITS.
method Online robust tensor recovery algorithm leveraging spatio-temporal correlations and local consistency.
result Significantly improved computational efficiency and high recovery accuracy.
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.
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.
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…
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.
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.
When sensors collect spatio-temporal data in a large geographical area, the existence of missing data cannot be escaped. Missing data negatively impacts the performance of data analysis and machine learning algorithms. In this paper, we study deep autoencoders for missing data imputation in spatio-temporal problems. We…
New method controls false detections in brain activity localization.
problem Statistical control of false detections in brain activity localization.
method Adapted Lasso estimator for spatio-temporal MEG/EEG data.
result Offers statistical guarantees and adaptive method for thresholding.
Estimates spatio-temporal Hawkes processes using tensor recovery.
problem Estimating influence functions for spatio-temporal Hawkes processes.
method Formulates influence function as a tensor kernel, assumes low-rank structure, solves as convex optimization problem.
result Provides theoretical guarantees and demonstrates efficiency with simulations.
Efficient spatio-temporal Gaussian process inference method.
problem Scalable Gaussian process inference for multivariate, spatio-temporal data.
method Combines spatio-temporal filtering with natural gradient variational inference, resulting in a scalable non-conjugate GP method.
result Linear scaling with respect to time and logarithmic scaling with respect to time steps.
In this study, the wind data series from five locations in Aegean Sea islands, the most active `hotspots' in terms of refugee influx during the Oct/2015 - Jan/2016 period, are investigated. The analysis of the three-per-site data series includes standard statistical analysis and parametric distributions, auto-correlati…
New algorithm predicts spatio-temporal events with improved accuracy.
problem Non-stationary spatio-temporal prediction on dense and sparse sequences.
method Probabilistic approach using point processes and self-organizing decision trees.
result Significant performance improvements over baseline and state-of-the-art methods.
Nonlocal Bayesian modeling for continuous spatio-temporal dynamics
problem Handling irregular time points, sparse observations, and nonlocal interactions in spatio-temporal forecasting
method Hierarchical Bayesian framework with coordinate-based spatial basis expansion and continuous-time ODE
result Strong forecasting and uncertainty calibration