Develops a method for reverse stress testing in multivariate scenarios.
problem Reconstructing a multivariate stress scenario from a single exogenous shock.
method Maximizing conditional density under three distributional assumptions.
result Simulated scenarios are economically coherent and reproduce risk-reward asymmetry.
In this paper, we present a novel approach to the generation of virtual scenarios of multivariate financial data of arbitrary length and composition of assets. With this approach, decades of realistic time-synchronized data can be simulated for a large number of assets, producing diverse scenarios to test and improve q…
Model handles missing data in partial blackouts for multivariate time series.
problem Missing values in multivariate time series data.
method Two-stage imputation process using self-attention and diffusion processes.
result Model effectively handles missing data during training and outperforms state-of-the-art.
A new notion of stochastic ordering is introduced to compare multivariate stochastic risk models with respect to extreme portfolio losses. In the framework of multivariate regular variation comparison criteria are derived in terms of ordering conditions on the spectral measures, which allows for analytical or numerical…
Paper proposes a copula method to generate unfavorable VaR scenarios.
problem Creating unfavorable VaR scenarios for insurance models.
method Patchwork copulas to create unfavorable VaR scenarios with given marginal distributions.
result Demonstrated with a 19-dimensional real-life insurance losses data set.
New method simulates multivariate extreme events using GANs and Aitchison coordinates.
problem Simulating multivariate extreme events for economic risk assessment.
method Wasserstein-Aitchison GAN approach combining tail dependence and marginal tail modeling.
result Strong performance in capturing tail dependence and generating accurate extreme observations.
Novel approach integrates Multivariate Square-root Lasso into Synthetic Control for high-dimensional data.
problem Challenges in practical implementation and computational efficiency of Synthetic Control method for high-dimensional disaggregated data.
method Integrates Multivariate Square-root Lasso into Synthetic Control framework.
result Demonstrates superior computational efficiency without compromising estimation accuracy.
New method assesses multivariate stochastic dominance using Optimal Transport.
problem Benchmarking models across multiple metrics considering dependencies.
method Characterization of multivariate first stochastic dominance via couplings, entropic regularization, and Optimal Transport.
result Established CLT and consistency for the empirical statistic, enabling hypothesis testing.
The univariate piecing-together approach (PT) fits a univariate generalized Pareto distribution (GPD) to the upper tail of a given distribution function in a continuous manner. We propose a multivariate extension. First it is shown that an arbitrary copula is in the domain of attraction of a multivariate extreme value …
Develops coresets for scalable multivariate distribution estimation.
problem Handling large-scale data in non-parametric or semi-parametric regression and density estimation.
method Novel coreset construction for multivariate conditional transformation models (MCTMs).
result Substantial data reduction with high log-likelihood accuracy.
We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in pr…
Extends geostatistical simulation method to handle multiple variables and large grids.
problem Scalability and handling of multiple variables in geostatistical simulation.
method Uses Sinkhorn optimal transport with sparse matcher and FFT-MA Gaussian backbone.
result MST-Direct reproduces joint distribution with zero histogram error and accurately preserves spatial correlation.
We establish a foundation for multivariate counterfactual identification using dynamic optimal transport.
problem Addressing the open question of counterfactual identification for high-dimensional multivariate outcomes from observational data.
method Establish a foundation for multivariate counterfactual identification using continuous-time flows, including non-Markovian settings, with tools from dynamic optimal transport.
result Characterise the conditions under which flow matching yields a unique, monotone, and rank-preserving counterfactual transport map, ensuring consistent inference.
New algorithm extracts shared latent space for cortico-muscular interactions.
problem Challenges of high dimensionality and limited sample sizes in multivariate cortico-muscular analysis.
method Structured and sparse partial least squares coherence (ssPLSC) algorithm.
result ssPLSC achieves competitive or better performance in scenarios with limited sample sizes and high noise levels.
InGRA models for efficient Granger causality learning in multivariate time series.
problem Efficiently modeling Granger causality in large-scale multivariate time series data.
method Inductive GRanger causal modeling (InGRA) framework with prototypical Granger causal attention.
result InGRA detects common causal structures and infers Granger causal structures for new individuals.
New method for robust financial portfolio analysis.
problem Challenges in modeling financial portfolio dependence structure.
method Nonparametric Angles-based Correlation (NAbC) method.
result Valid inferences and flexible scenarios for portfolio analysis.
New ensemble models classify mouse movement trajectories to assess survey question difficulty.
problem Assessing survey question difficulty based on respondents' interaction data.
method Ensemble models combining semi-metric-based weak learners to classify multivariate functional data.
result Improved survey data quality through better identification of respondent difficulty.
Chronos-2 forecasts multivariate and covariate data without task-specific training.
problem Limited applicability of existing time series forecasting models to real-world multivariate and covariate data.
method Chronos-2 uses a group attention mechanism for in-context learning across multiple time series.
result Chronos-2 achieves state-of-the-art performance across comprehensive benchmarks.
A new method using energy distance for ensemble and scenario reduction.
problem Solving complex dynamic and stochastic programs, especially in energy systems.
method Proposes a new method based on energy distance for ensemble and scenario reduction.
result Reduced scenario sets exhibit better statistical properties for energy distance than Wasserstein distance.
MTSCI uses diffusion models to impute multivariate time series data with consistency.
problem Imputation of missing values in multivariate time series data.
method MTSCI employs a contrastive complementary mask and mixup mechanism to ensure intra-consistency and inter-consistency.
result MTSCI achieves state-of-the-art performance on multivariate time series imputation tasks.
Paper proposes a new method for probabilistic electricity price forecasting.
problem Accurate estimation of forecast uncertainties for optimal decision making.
method Implicit generative ensemble post-processing using an ensemble of point forecasting models.
result Method outperforms well-established model combination benchmarks.
Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. T…
The paper proposes a parallelizable clustering method for multivariate data.
problem The standard model-based clustering method assumes the same number of clusters per margin, which is often unrealistic.
method Developed a finite mixture model per margin with different numbers of clusters, and used a game-inspired algorithm to cluster multivariate data.
result The proposed method shows good performance in various scenarios and real datasets.
Skeleton clustering detects clusters in high-dimensional data without needing prototypes.
problem Detecting clusters in high-dimensional data with irregular shapes.
method Skeleton clustering combines prototype methods, density-based clustering, and hierarchical clustering using surrogate density measures.
result Skeleton clustering reliably detects clusters in multivariate and high-dimensional data.
Researchers forecast VoIP traffic in mobile networks using multivariate time series analysis.
problem Predicting VoIP traffic behavior in real mobile networks for better resource allocation.
method Multivariate time series analysis, Vector Autoregressive models, machine learning techniques.
result Forecasting accuracy and insights into VoIP traffic dynamics.
Improved learning of multivariate Gaussians with imperfect advice.
problem Learning multivariate Gaussians with inaccurate advice.
method Developed learning algorithms for multivariate Gaussians using imperfect advice.
result Achieved better sample complexity for learning multivariate Gaussians with imperfect advice.
Testing two potentially multivariate variables for statistical dependence on the basis finite samples is a fundamental statistical challenge. Here we explore a family of tests that adapt to the complexity of the relationship between the variables, promising robust power across scenarios. Building on the distance correl…
Proposes a new model for joint probability distributions in computer vision.
problem Limitation of existing models in meeting diverse downstream tasks.
method Uses parametric conditional probability distributions for each group of variables conditioned on the rest.
result Models can be used for any downstream task without task-specific design.
CRC improves multivariate forecasting accuracy without risking performance degradation.
problem Systematic errors and lack of guarantees in multivariate forecasters.
method CRC uses a causality-inspired encoder and hybrid corrector with a safety mechanism.
result CRC consistently improves accuracy and ensures high non-degradation rates.
This paper investigates the utilization of maximum and average distance correlations for multivariate independence testing. We characterize their consistency properties in high-dimensional settings with respect to the number of marginally dependent dimensions, compare the advantages of each test statistic, examine thei…
Proposes SROF for row-wise fusion in federated learning for multivariate responses.
problem Heterogeneous client models with shared variable-level structure.
method Sparse Row-wise Fusion (SROF) regularizer and RowFed algorithm.
result Empirically shows consistent error reduction and stronger variable-level cluster recovery.
Proposes a method to compute information theory measures via Gaussianization.
problem Challenges of computing information from multidimensional data.
method Indirect computation using a multivariate Gaussianization transform.
result Proposed methods outperform existing estimators, especially in high dimensions.
EagleEye detects localized density anomalies in multivariate data.
problem Identifying signal events, regime changes, or model mismatch in scientific data.
method EagleEye pinpoints local over- and under-densities by assigning anomaly scores based on binary membership sequences and binomial null models.
result EagleEye can detect genuine local anomalies and estimate background purity.
BayOTIDE tackles imputation of irregularly sampled multivariate time series with uncertainty quantification.
problem Imputation of irregularly sampled multivariate time series with missing values and noises.
method BayOTIDE treats multivariate time series as a combination of low-rank temporal factors with different patterns, using Gaussian Processes (GPs) as functional priors and converting them into state-space priors for scalable online inference.
result BayOTIDE can handle imputation over arbitrary time stamps and offers uncertainty quantification and interpretability.
SBAMDT uses adaptive soft splits to model complex decision boundaries.
problem Limited ability of standard decision trees to capture complex decision boundaries.
method Probabilistic additive decision tree model with adaptive soft multivariate splits.
result Demonstrated improved predictive performance on synthetic and real datasets.
In this paper, we present a two-stage stochastic international portfolio optimisation model to find an optimal allocation for the combination of both assets and currency hedging positions. Our optimisation model allows a "currency overlay", or a deviation of currency exposure from asset exposure, to provide flexibility…
A broad range of cross-m-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as m increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i…
Study examines how different time series cross-validation methods affect anomaly detection in multivariate time series.
problem Evaluating anomaly detection in multivariate time series requires preserving temporal dependencies, especially for subsequence anomalies.
method Systematically investigates walk-forward and sliding window methods across various validation configurations and classifier types.
result Sliding window method consistently yields higher precision-recall scores and reduced fold-to-fold performance variance, particularly for deep learning models.
FCPCA fuzzy clusters high-dimensional time series data efficiently.
problem Ambiguous clustering of multivariate time series data with overlapping distributions.
method FCPCA based on common principal component analysis.
result FCPCA outperforms existing methods in fuzzy clustering of multivariate time series.
DRIO improves time series imputation by minimizing reconstruction error and distributional divergence.
problem Bias in imputation due to mismatch between observed and true data distributions.
method DRIO minimizes reconstruction error and worst-case divergence using Wasserstein ambiguity set.
result DRIO consistently provides robust imputation and improved forecasting.
New method detects biomarker-treatment interactions in clinical trials.
problem Detecting interactions between high-dimensional biomarkers and treatments in randomized trials.
method Two-stage penalized regression screening using ridge regression for multivariate screening.
result Ridge regression screening provides greater power than traditional methods in correlated data.
CoIFNet unifies imputation and forecasting for robust multivariate time series prediction with missing values.
problem Pervasive missing values degrade multivariate time series forecasting accuracy.
method CoIFNet integrates imputation and forecasting through Cross-Timestep Fusion and Cross-Variate Fusion modules.
result CoIFNet achieves 24.40% improvement over state-of-the-art methods at 0.6 point (block) missing rate.
This paper solves the convergence problem for estimating MGGD parameters with a convex formulation.
problem Establishing convergence properties for estimating MGGD parameters with unknown mean and precision matrix.
method Proposes a convex formulation with well-established convergence properties for robust estimation in noisy scenarios.
result Demonstrates improved accuracy in precision and covariance matrix estimation compared to existing methods.
New model predicts energy prices under different scenarios.
problem Complex causal relationships in energy markets with continuous regime changes.
method Augmented Time Series Structural Causal Models (ATSCM) integrating neural causal discovery.
result Enables novel counterfactual queries in energy markets.
Paper presents adaptive minimax risk classifiers for multidimensional concept drift.
problem Multidimensional concept drift in supervised classification.
method Adaptive minimax risk classifiers (AMRCs) tracking multivariate and high-order distribution changes.
result AMRCs provide computable tight performance guarantees and improve classification.
MPPN network improves long-term time series forecasting accuracy.
problem Inaccurate long-term time series forecasting due to noise and lack of interpretability.
method MPPN network constructs context-aware multi-resolution semantic units and employs multi-periodic pattern mining and channel adaptive module.
result MPPN significantly outperforms state-of-the-art methods on nine real-world benchmarks.
TabCF simplifies causal inference for distributional effects.
problem Estimating causal effects with unmeasured confounding.
method Control function regression using tabular foundation models.
result Accurate, fast, and transparent causal estimation of distributional quantities.
We consider a multi-objective risk-averse two-stage stochastic programming problem with a multivariate convex risk measure. We suggest a convex vector optimization formulation with set-valued constraints and propose an extended version of Benson's algorithm to solve this problem. Using Lagrangian duality, we develop sc…