A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method improves convergence of spatial filters in neural networks.
Infinite CNNs lose spatial correlations, but can be restored by correlated weights.
CaLoNet integrates spatial and local correlations for multivariate time series classification.
A new model predicts financial volatility across firms using spatial correlations.
Spatially relaxed inference tackles high-dimensional linear models with correlated covariates.
CMoS improves time series forecasting with minimal parameters.
Estimates mean of distributed vectors with sparsification and spatial/temporal correlations.
S-SIRUS explains RF for spatial data, improving accuracy and interpretability.
Bayesian method improves segmentation accuracy with noisy labels.
Hybrid model predicts flow and pressure in water systems.
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…
A framework for navigating environments with spatially correlated obstacles and uncertain blockage status.
E-LMC improves spatial field prediction accuracy by linearizing complex fields.
AGCRN forecasts traffic using adaptive graph and recurrent learning.
STCA discovers dynamic functional brain networks using spatial-temporal convolution and attention.
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…
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.
Study predicts climate data at distant locations using machine learning.
Enhanced deep learning model forecasts household leverage series accurately.
Geostatistical learning faces unique challenges due to spatial correlation and covariate shifts.
New neural network captures spatial correlations in wind speed predictions.
SPACY discovers causal graphs from spatiotemporal data using variational inference.
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,…
Gaussian processes (GPs) are commonplace in spatial statistics. Although many non-stationary models have been developed, there is arguably a lack of flexibility compared to equipping each location with its own parameters. However, the latter suffers from intractable computation and can lead to overfitting. Taking the i…
A2-SBNN models spatial data with copulas for non-Gaussian dependencies.
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…
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.
CNNs predict spatial fields from sparse data.
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Tensor latent factor models can describe higher-order correlations for spatial data. However, they are computationally expensive to train and are sensitive to initialization, leading to spatially incoher…
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…
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of …
Spatial information is not always necessary for spatio-temporal models.
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). …
New algorithm balances spatial data approximation and prediction accuracy.
The paper optimizes spatial experimental designs to improve causal effect estimation.
A new model predicts spatially varying inland flooding from time-varying inputs.
Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.
State-of-the-art deep learning methods have shown a remarkable capacity to model complex data domains, but struggle with geospatial data. In this paper, we introduce SpaceGAN, a novel generative model for geospatial domains that learns neighbourhood structures through spatial conditioning. We propose to enhance spatial…
Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.
A new tensor network method for image classification reduces computation cost.
We propose a probabilistic model for inferring the multivariate function from multiple areal data sets with various granularities. Here, the areal data are observed not at location points but at regions. Existing regression-based models can only utilize the sufficiently fine-grained auxiliary data sets on the same doma…
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…
Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps. Cl…
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…