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.
Study predicts climate data at distant locations using machine learning.
Predicts user next location using CDR data.
MPE models traffic trajectory data to predict next locations.
Paper proposes a graph neural network for accurate long-term ILI prediction.
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
CMDRNN predicts user location using WiFi fingerprints with deep learning.
Satellite images improve real-estate price predictions.
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' mov…
ε-shotgun optimizes expensive function evaluations in parallel.
Neural network predicts turbulence from wall shear stress.
The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecast…
In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d Gaussian distribution…
Assessing the predictive accuracy of black box classifiers is challenging in the absence of labeled test datasets. In these scenarios we may need to rely on a human oracle to evaluate individual predictions; presenting the challenge to create query algorithms to guide the search for points that provide the most informa…
Graph neural networks improve residential location choice predictions.
New method improves spatial prediction validation accuracy.
Calendar graph neural networks model user behavior with location and time data.
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…
Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture…
Deep learning predicts user identity, activity, and location from Wi-Fi signals.
Deep learning has been used in many areas, such as feature detections in images and the game of go. This paper presents a study that attempts to use the deep learning method to predict turbomachinery performance. Three different deep neural networks are built and trained to predict the pressure distributions of turbine…
NAMLSS models provide interpretable neural regression for location, scale, and shape.
Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may b…
TraLFM models human mobility patterns from traffic trajectories.
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g.~operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not …
Spatial blind source separation simplifies multivariate spatial prediction.
Interpretable ML models predict recidivism as well as non-interpretable methods and are more fair.
Paper proposes a GAN-based approach for RTLMP prediction.
Meta-data from photo-sharing websites such as Flickr can be used to obtain rich bag-of-words descriptions of geographic locations, which have proven valuable, among others, for modelling and predicting ecological features. One important insight from previous work is that the descriptions obtained from Flickr tend to be…
This work proposes a student-teacher network for predicting hospital admission locations.
Method reduces model bias in water temperature prediction using physics-guided GNNs.
Bayesian model improves traffic prediction with uncertainty estimates.
MIP framework improves urban flow prediction by adapting to distribution shifts.
Two deep learning models improve indoor location prediction from WiFi fingerprints.
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving…
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate prediction model for these tasks usually requires a large number of images manually annotated with labels and finding sites of abnormalities. In …
Study uses neural networks to predict wall quantities in turbulent flows.
Study develops a new model for predicting individual mobility based on activity patterns.
An important task in structural design is to quantify the structural performance of an object under the external forces it may experience during its use. The problem proves to be computationally very challenging as the external forces' contact locations and magnitudes may exhibit significant variations. We present an e…
Statistical learning approach for spatial data prediction.
Sparse Gaussian Processes improve scalability by learning inducing points from data.
This paper benchmarks Bayesian models' ability to estimate predictive correlations, especially for active learning.
Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise …
We utilize Wi-Fi communications from smartphones to predict their mobility mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four strategic locations in a closed loop on streets in downtown Toronto. Deep neural network (Multilayer Perceptron) along with three decision tree based classifiers (Decisi…
Deep neural networks predict earthquake locations with high accuracy.
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs. However, existing Graph Neural Network (GNN) architectures have limited power in capturing the position/location of a given node with respect to all other nodes of the graph. Here w…
Predicts destinations and routes from partial trajectory data.
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into r…
New method predicts y distributions from imperfect data.