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.
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
Estimates spatio-temporal Hawkes processes using tensor recovery.
The collaborative ranking problem has been an important open research question as most recommendation problems can be naturally formulated as ranking problems. While much of collaborative ranking methodology assumes static ranking data, the importance of temporal information to improving ranking performance is increasi…
A new method for filling in missing traffic data improves accuracy over existing techniques.
Proposes a new tensor decomposition method for functional temporal data with adaptive complexity.
New ranking models for time series data using GARCH-type approach.
Paper proposes LATC for multivariate time series prediction and missing data imputation.
Temporal information impacts only a fraction of time series datasets, skewing benchmark evaluations.
Robust PCA detects anomalies and fills gaps in seasonal time series data.
Method improves clarity in forecasting spatio-temporal data.
New method improves traffic speed estimation from sparse data.
Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis. Existing streaming tensor factorization algorithms rely on least-squares data fitting and they do not possess a mechanism for tensor rank dete…
Paper proposes AI for stock market forecasting using external knowledge.
We consider -way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library. Our method, Shape Constrained Tensor Decomposition (SCTD) is based upon the CANDECOMP/PARAFAC (CP) decomposition which produces -rank appr…
Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice…
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.
Paper introduces a novel framework for recognizing dynamic ranking structures in preference-based data.
NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.
Temporal Functional Circuits explain KAN forecasts with interpretable edge functions.
This paper reviews methods for discovering patient subgroups from EHR data.
In this paper, we investigate the statistical convergence rate of a Bayesian low-rank tensor estimator. Our problem setting is the regression problem where a tensor structure underlying the data is estimated. This problem setting occurs in many practical applications, such as collaborative filtering, multi-task learnin…
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical obs…
Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focused on tactical combat. Mastering MOBAs requires extensive practice, as is exemplified in the popular MOBA Defence o…
Framework detects and ranks suspicious market manipulation using temporal convolutions and expert assessment.
We consider dynamic pricing with many products under an evolving but low-dimensional demand model. Assuming the temporal variation in cross-elasticities exhibits low-rank structure based on fixed (latent) features of the products, we show that the revenue maximization problem reduces to an online bandit convex optimiza…
Training deep neural networks with spatio-temporal (i.e., 3D) or multidimensional convolutions of higher-order is computationally challenging due to millions of unknown parameters across dozens of layers. To alleviate this, one approach is to apply low-rank tensor decompositions to convolution kernels in order to compr…
New model captures patient-level EHR data efficiently.
Develops a deep non-stationary kernel for non-stationary spatio-temporal point processes.
New protocol evaluates synthetic data for temporal consistency.
A new model detects and localizes anomalies in multivariate time series data.
This paper evaluates various loss functions for Transformer models in stock ranking.
This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d. Gaussian random sample, we establish high dimensional rates of convergence to…
We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF). We extend this framework to account for heteroscedasticity on the output labels(i.e., pain intensity scores) and introduce a novel dynamic features, dynamic ra…
We study the problem of detecting an abrupt change to the signal covariance matrix. In particular, the covariance changes from a "white" identity matrix to an unknown spiked or low-rank matrix. Two sequential change-point detection procedures are presented, based on the largest and the smallest eigenvalues of the sampl…
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…
A new method reduces high-dimensional filtering to quadratic complexity.
Paper proposes an active learning method for surgical workflow recognition using long-range temporal dependency.
Neural network factorization speeds up Vlasov equation simulations.
SPINEX improves time series forecasting with explainable neighbors.
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…
The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Day-ahead price forecasting is cast as a…
Enhances VAR model estimation using transfer learning.
SALT models combine ARHMM and SLDS for efficient, interpretable time-series analysis.
Modular pipeline improves stock portfolio prediction robustness under regime changes.
Low-rank matrix factorizations arise in a wide variety of applications -- including recommendation systems, topic models, and source separation, to name just a few. In these and many other applications, it has been widely noted that by incorporating temporal information and allowing for the possibility of time-varying …
Fine-grained action segmentation in long untrimmed videos is an important task for many applications such as surveillance, robotics, and human-computer interaction. To understand subtle and precise actions within a long time period, second-order information (e.g. feature covariance) or higher is reported to be effectiv…
TASC improves synthetic control for time-series data with trends.