New method improves convergence of spatial filters in neural networks.
problem Poor convergence behavior of spatial filters in neural networks.
method Correlated initialization for spatial filters.
result Uncorrelated initialization leads to poor convergence and slow training of some parameters.
Infinite CNNs lose spatial correlations, but can be restored by correlated weights.
problem Infinite CNNs lose spatial correlations, which are crucial for their performance.
method Introduced correlated weights to restore spatial correlations in infinite CNNs.
result Optimal performance is achieved with a moderate level of weight correlation.
CaLoNet integrates spatial and local correlations for multivariate time series classification.
problem Ignoring spatial and local correlations in multivariate time series classification.
method Model spatial correlations using causality modeling, extract local correlations, integrate into graph neural network.
result Competitive performance compared to state-of-the-art methods on UEA datasets.
A new model predicts financial volatility across firms using spatial correlations.
problem Predicting financial volatility across firms in a network.
method Heterogeneous spatiotemporal GARCH model with local likelihood estimation.
result The model captures spatial spillovers and contagion effects in financial networks.
Spatially relaxed inference tackles high-dimensional linear models with correlated covariates.
problem Accurate inference is challenging in high-dimensional settings with spatially correlated covariates.
method Proposes ensembled clustered inference algorithms that control the δ-FWER under standard assumptions. result Ensembled clustered inference algorithms control the δ-FWER and achieve decent power. CMoS improves time series forecasting with minimal parameters.
problem Efficiently forecasting time series data with limited resources.
method CMoS directly models chunk-wise spatial correlations, using Correlation Mixing and Periodicity Injection techniques.
result CMoS outperforms state-of-the-art models with minimal parameters.
Estimates mean of distributed vectors with sparsification and spatial/temporal correlations.
problem Estimating mean of high-dimensional vectors distributed across nodes with low communication cost.
method Modifies decoding method to leverage spatial and temporal correlations in sparsified vectors.
result Estimators consistently outperform more sophisticated sparsification methods.
Bayesian model captures spatial correlations in data.
problem Modeling spatial correlations in high-dimensional data.
method Structured Bayesian Gaussian process latent variable model with parameterized spatial kernel and structure-exploiting algebra.
result Inference is tractable with computational complexity similar to traditional Bayesian GP-LVM.
S-SIRUS explains RF for spatial data, improving accuracy and interpretability.
problem Non-interpretable nature of Random Forest in spatially dependent data.
method Proposes S-SIRUS, a spatial extension of SIRUS for extracting interpretable rules.
result S-SIRUS outperforms SIRUS in spatially dependent data, offering higher predictive accuracy and shorter rule lists.
Bayesian method improves segmentation accuracy with noisy labels.
problem Annotation errors in semantic segmentation due to mislabeling and spatial correlations.
method Approximate Bayesian estimation with spatially correlated discrete distributions and variational inference.
result The method achieves performance comparable to clean labels under moderate noise levels.
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.
Correlated component analysis as proposed by Dmochowski et al. (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are identical. Here we propose a hierarchical pr…
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…
Hybrid VAE-GAN model generates sequential data with spatial correlations.
problem Generating sequences with spatial correlations between frames.
method Semi-recurrent CNNs in encoder, generator, and discriminator; sampling subsequent frames from latent distributions.
result Maintains dependencies between frames in generated sequences.
A framework for navigating environments with spatially correlated obstacles and uncertain blockage status.
problem Navigation in environments with spatially correlated obstacles of uncertain blockage status.
method Modeling spatial correlation with Gaussian Random Field, developing Bayesian belief updates, proposing a two-stage learning framework with offline and online phases.
result Consistent performance gains over baselines in environments with adversarial interruptions or clustered natural hazards.
E-LMC improves spatial field prediction accuracy by linearizing complex fields.
problem Predicting complex spatial fields with high accuracy and efficiency.
method Introducing an invertible neural network to linearize nonlinear spatial fields, enabling the use of LMC for nonlinear problems.
result Maximum improvement of about 40% over original LMC, outperforming other models.
AGCRN forecasts traffic using adaptive graph and recurrent learning.
problem Forecasting traffic dynamics with complex spatial and temporal correlations.
method Adaptive Graph Convolutional Recurrent Network (AGCRN) with Node Adaptive Parameter Learning (NAPL) and Data Adaptive Graph Generation (DAGG).
result AGCRN outperforms state-of-the-art models without pre-defined graphs.
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.
We investigate the behavior of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is presumed to oscillate and damp in a quantum spatial-periodic harmonic oscillator potential well. Complicated non-linear relations including inter-band positive correlatio…
Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and fu…
Flexible non-stationary modeling of spatial outcomes using a mixed-stationary Gaussian process.
problem Limited flexibility in non-stationary models and computational intractability.
method Developed a non-stationary Gaussian process with individually set stationarity parameters at each location, using a non-parametric mixture model to reduce parameters and incorporate spatial correlation.
result Improved prediction efficiency through spatially correlated components in the mixture model.
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes. Current research works that consider spatial information are mainly focused on the linear mixing model. In this paper, we investigate a …
Deep learning model predicts traffic flows across entire network for multiple steps ahead.
problem Accurately forecasting future traffic flows across all network links.
method Spatial-Temporal Sequence to Sequence (STSeq2Seq) model combining seq2seq and graph convolution.
result STSeq2Seq achieves state-of-the-art performance in traffic forecasting.
Study predicts climate data at distant locations using machine learning.
problem Predict climate variables at distant locations where comprehensive data collection is not feasible.
method Uses reservoir computing and vector autoregression models for prediction.
result Machine learning improves prediction accuracy for highly correlated data.
Geostatistical learning faces unique challenges due to spatial correlation and covariate shifts.
problem Challenges in applying statistical learning to geospatial data.
method Assessing generalization error under covariate shift and spatial correlation.
result No classical learning methods are adequate for model selection in geospatial contexts.
Enhanced deep learning model forecasts household leverage series accurately.
problem Forecasting household leverage series due to complex temporal-spatial dynamics.
method TSEN model with multiple RNN-based layers and an attention layer.
result Captures temporal-spatial dynamics and provides more accurate predictions.
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.
New neural network captures spatial correlations in wind speed predictions.
problem Uncertainty quantification in neural network predictions for high-dimensional, correlated data.
method Training neural networks with multidimensional Gaussian loss, preserving spatial correlation and computational tractability.
result Demonstrated super-resolution of surface wind speed with explicit correlation modeling.
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.
One major hurdle in the road toward a low carbon economy is the present entanglement of developed economies with oil. This tight relationship is mirrored in the correlation between most of economic indicators with oil price. This paper addresses the role of oil compared to the other three main energy commodities -coal,…
A2-SBNN models spatial data with copulas for non-Gaussian dependencies.
problem Capturing complex spatial relationships and extreme dependencies in non-Gaussian data.
method Embedding A2 copula into a Bayesian neural network, trained with Wasserstein loss and moment matching.
result A2-SBNN consistently delivers high accuracy across various dependency strengths.
It is ubiquitous in natural and social sciences that two variables, recorded temporally or spatially in a complex system, are cross-correlated and possess multifractal features. We propose a new method called multifractal detrended cross-correlation analysis (MF-DXA) to investigate the multifractal behaviors in the pow…
MRTL learns interpretable spatial patterns efficiently.
problem Efficient and interpretable spatial analysis in various fields.
method Multiresolution Tensor Learning (MRTL) algorithm.
result 4~5x speedup with accurate and interpretable latent factors.
We study velocity correlations induced by diffusion and dissipation in a simple dissipative dynamical system. We observe that diffusion, as a result of time reversible microscopic processes, leads to correlations with different spatial parity from those caused by dissipation, consisting of time irreversible microscopic…
A3T-GCN improves traffic forecasting by capturing spatial and temporal dependencies.
problem Accurate real-time traffic forecasting in complex road networks.
method Attention Temporal Graph Convolutional Network (A3T-GCN) integrating recurrent units and graph convolutional network.
result Improved prediction accuracy through attention mechanism and global temporal information.
CNNs predict spatial fields from sparse data.
problem Predicting complete spatial fields from limited observations.
method Convolutional Neural Networks (CNNs) trained on a single partially observed field.
result CNNs can flexibly capture local spatial patterns without explicit covariance modeling.
We present a simple one-parameter model for spatially localised evolving agents competing for spatially localised resources. The model considers selling agents able to evolve their pricing strategy in competition for a fixed market. Despite its simplicity, the model displays extraordinarily rich behavior. In addition t…
We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). …
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.
New algorithm balances spatial data approximation and prediction accuracy.
problem Lack of methods considering spatial correlation and downstream modeling in dimension reduction.
method Formalizes approximation and modeling utility as metrics, proposes a balanced algorithm.
result Optimal trade-off between approximation accuracy and downstream modeling utility.
DSTP-RNN improves long-term multivariate time series prediction using attention-based RNN.
problem Long-term prediction of multivariate time series with spatial correlations and spatio-temporal relationships.
method Inspired by human attention mechanism, DSTP-RNN uses a dual-stage two-phase structure and multiple attentions to enhance spatial correlations and long-term dependence.
result DSTP-RNN outperforms nine baseline methods on four datasets in energy, finance, environment, and medicine.
Model for inferring multivariate functions from areal data.
problem Inferring multivariate functions from areal data with varying granularities.
method Probabilistic model using Gaussian processes with spatial aggregation.
result Model effectively estimates spatial correlations and dependencies between areal data sets.
The paper optimizes spatial experimental designs to improve causal effect estimation.
problem Optimizing spatial experimental designs to enhance causal effect estimation accuracy.
method Proposes a surrogate function for MSE and uses graph cut algorithms to learn optimal designs.
result The method accommodates spatial interference and covariance, is computationally efficient, and validated by theoretical and numerical experiments.
A new model predicts spatially varying inland flooding from time-varying inputs.
problem Ignoring time series and spatial correlations in flood models leads to inaccurate predictions.
method Introduced a multioutput Gaussian process model with separable kernels for functional inputs and spatial locations.
result The model provides accurate predictions of spatially varying inland flooding with minimal computational time.
Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.
problem Statistical inference challenges with missing at random labels and spatial dependence.
method Doubly robust estimator with cross-fit nuisances and jackknife spatial HAC variance correction.
result Asymptotically valid confidence intervals with improved finite-sample calibration.
Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.
problem Understanding the mechanism captured by machine learning models in air pollution studies, especially with spatial correlation.
method Leave-one-out approach for variable importance measure applicable to models with separable mean and covariance components.
result The new method highlights differences in model mechanisms even for similar prediction accuracies.
Model refines coarse spatial data using diverse auxiliary data sets.
problem Tackles the challenge of refining coarse spatial data with varying auxiliary data granularities.
method Proposes a probabilistic model using Gaussian processes to hierarchically incorporate auxiliary data sets of various granularities.
result Can effectively refine coarse-grained spatial data using auxiliary data sets of different granularities.
A new tensor network method for image classification reduces computation cost.
problem Efficiently classifying images in high-dimensional spaces.
method Proposes a multi-layered tensor network (MLTN) that performs one MPS operation per layer, reducing computation cost.
result Reduces computation cost without degrading performance.