Study compares univariate vs multivariate models for electricity price forecasting.
problem Optimal model structure for short-term electricity price forecasting.
method Comprehensive empirical study comparing univariate and multivariate modeling frameworks.
result Multivariate models do not uniformly outperform univariate models across all datasets, seasons, or hours.
This study compares multivariate vs univariate machine learning for multi-output regression.
problem When to use multivariate ensemble techniques over separate univariate models.
method Comparative analysis of different multivariate approaches for multi-output regression.
result Multivariate ensemble techniques outperform separate univariate models in simulations.
A graph neural network improves multivariate post-processing of ensemble forecasts.
problem Systematic biases in ensemble forecasts and loss of dependencies across forecast dimensions.
method A composite-Loss Graph Neural Network (dualGNN) trained with a composite loss function combining ES and VS.
result The dualGNN outperforms traditional methods in multivariate verification metrics and captures spatial relationships.
Meta algorithm solves multivariate optimization using univariate optimizers.
problem Multivariate global optimization problems.
method Meta algorithm combining univariate global optimizers.
result Meta algorithm provides robust regret guarantees.
New model predicts univariate and multivariate time series with improved accuracy.
problem Complex patterns in univariate and multivariate time series forecasting.
method Uses autoregressive convolutional recurrent neural network with feature extraction and recurrent encoder.
result Outperforms existing architectures in multivariate time series datasets.
Statistical tests that compare classification algorithms are univariate and use a single performance measure, e.g., misclassification error, F measure, AUC, and so on. In multivariate tests, comparison is done using multiple measures simultaneously. For example, error is the sum of false positives and false negatives…
New risk measures for multivariate data, consistent and decomposable.
problem Developing consistent risk measures for multiple variables.
method Showed strong consistency leads to decomposition into aggregation and univariate risk.
result Multivariate risk measures are conditional certainty equivalents under strong consistency.
New simulations advise caution in choosing principal components for multivariate functional data.
problem Inaccurate selection of principal components in multivariate functional data.
method Extensive simulations investigating the reliability of percentage of variance explained thresholds.
result Conventional threshold methods may fail to accurately explain overall variance in multivariate functional data.
AdaPTS adapts univariate FMs for multivariate time series forecasting.
problem Challenges in managing feature dependencies and uncertainty quantification in multivariate time series forecasting.
method Adapters that transform multivariate inputs into a latent space and apply univariate FMs independently to each dimension.
result AdaPTS enhances forecasting accuracy and uncertainty quantification compared to baseline methods.
Hybrid method selects fewer genes for cancer classification.
problem Selecting genes for cancer classification from microarray data.
method Hybrid of univariate (LIK) and multivariate (RFE) feature selection methods.
result Hybrid method selects fewer genes with similar or better accuracy.
New multivariate risk measures improve on univariate OCE methods.
problem Improving risk assessment in multivariate settings.
method Inspired by univariate OCE, introduces convex, monotonic, cash-invariant measures.
result Numerical algorithms provide error estimates for computations.
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.
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 …
New algorithms benchmarked for multivariate time series classification.
problem Comparing algorithms for multivariate time series classification.
method Review and comparison of recent MTSC algorithms using the UEA archive.
result HIVE-COTE ensemble is most accurate for MTSC, but dynamic time warping is competitive.
Multivariate boosted trees improve forecasting and control by capturing correlated predictions.
problem Capturing multivariate target cross-correlations and applying structured penalties to predictions.
method A computationally efficient algorithm for fitting multivariate boosted trees.
result Multivariate trees outperform univariate counterparts in correlated prediction scenarios.
In this paper, we introduce two alternative extensions of the classical univariate Value-at-Risk (VaR) in a multivariate setting. The two proposed multivariate VaR are vector-valued measures with the same dimension as the underlying risk portfolio. The lower-orthant VaR is constructed from level sets of multivariate di…
Extended univariate Range Value-at-Risk to multivariate settings.
problem Inability of traditional risk measures for heavy-tail distributions and infinite tail expectations.
method Multivariate definitions of robust truncated tail expectations, robustness and properties derived, closed-form expressions and special cases discussed.
result Empirical estimators accuracy examined through numerical and graphical examples.
New method for multivariate distribution regression using NPT metric.
problem Regression with multivariate distributional responses and Euclidean predictors.
method Fréchet regression with nonparanormal transport (NPT) metric.
result Efficient estimation and granular interpretation of predictor effects.
Develops SQR models for multivariate exponential families allowing positive dependencies.
problem Lack of positive dependencies in multivariate graphical models for exponential and Poisson distributions.
method Introduces Square Root Graphical Models (SQR) derived from univariate exponential distributions, with methods for parameter estimation and likelihood approximation.
result Allows for arbitrary positive and negative dependencies in multivariate distributions without constraints on parameter values.
Proposes MVG-CRPS for robust multivariate forecasting.
problem Outliers in multivariate forecasting lead to significant errors.
method Integrates CRPS for MVG distributions, optimizing with MVG-CRPS.
result Improves robustness, accuracy, and uncertainty quantification.
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
problem Challenges in handling complex temporal-contextual relationships in multivariate time series forecasting.
method ARM is an enhanced multivariate LTSF architecture that employs Adaptive Univariate Effect Learning, Random Dropping, and Multi-kernel Local Smoothing.
result ARM outperforms vanilla Transformers on multiple benchmarks without significantly increasing computational costs.
New adaptive strategy for active learning with smooth boundaries.
problem Adaptive active learning in multivariate classification with unknown distributional parameters.
method Combining insights from recent works, reduction to univariate-adaptive strategies.
result Near-optimal rates achieved without prior knowledge of distributional parameters.
Method estimates multivariate counterfactual distributions efficiently and accurately.
problem Estimating multivariate counterfactual distributions in causal models with correlation structures.
method Proposes a method leveraging a one-dimensional subspace to capture correlation structures and efficiently estimate multivariate counterfactual distributions.
result Demonstrates superior performance over existing methods on synthetic and real-world data.
Study proposes a new portfolio selection method using non-Gaussian models and Esscher transform.
problem Portfolio selection with complex stock return structures and skewness, kurtosis.
method Multivariate non-Gaussian models (NTS and GH), Esscher transform for risk-neutral measure, simultaneous calibration of univariate log-returns and volatility.
result Demonstrated the effectiveness of the proposed models in fitting and selecting portfolios.
Review of multivariate Poisson-based distributions for count data.
problem Dependencies in high-dimensional count data.
method Categorization and empirical comparison of multivariate Poisson-based distributions.
result Empirical comparison of multivariate Poisson-based distributions on real-world datasets.
The paper bounds solutions to complex optimization problems with uncertain data.
problem Distributionally robust optimization problems with multivariate uncertainty sets.
method Conditions and bounds derived for multivariate and univariate Wasserstein distances, Bregman-Wasserstein divergences, and signed Choquet integrals.
result Computable lower and upper bounds for DRO problems, derived from scalar-valued aggregation functions and Wasserstein distances.
In economics, insurance and finance, value at risk (VaR) is a widely used measure of the risk of loss on a specific portfolio of financial assets. For a given portfolio, time horizon, and probability α, the 100α% VaR is defined as a threshold loss value, such that the probability that the loss on the portfolio ove…
Improved time series classification with GRU-FCN model.
problem Time series classification challenges.
method Hybrid LSTM-GRU model for univariate time series classification.
result GRU-FCN model outperforms state-of-the-art models.
PySAD offers a unified Python framework for efficient streaming anomaly detection.
problem Efficient anomaly detection in streaming data with strict constraints.
method Unified architecture with 17+ streaming algorithms, specialized components, and support for multiple learning paradigms.
result PySAD enables real-time processing with bounded memory and is compatible with other Python frameworks.
TS-Fault benchmarks TSF models against structural faults.
problem Evaluating the robustness of time series forecasting models against structured events.
method TS-Fault uses parameterized fault scenarios with controllable difficulty.
result Three findings contradict common leaderboard intuition.
A new Weyl prior is proposed for Bayesian statistics, offering a more canonical choice for parameter α.
problem Choosing a prior distribution for Bayesian inference.
method Proposed a new Weyl prior based on the Weyl structure on a statistical manifold.
result The Weyl prior is a special case of the α-parallel prior with α = -n, where n is the dimension of the statistical manifold.
Proves existence and uniqueness of optimal trading strategy for multivariate returns.
problem Finding optimal trading strategy for multiple asset returns.
method Proves existence and uniqueness of optimal solution using fractional trading ansatz.
result Optimal trading strategy can be numerically found using steepest ascent methods.
New MTSC archive adds 30 multivariate time series datasets for evaluation.
problem Lack of multivariate time series datasets for rigorous evaluation.
method Forming the first MTSC archive with 30 datasets, equal lengths, no missing data, and train/test splits.
result Addresses the need for a comprehensive evaluation of multivariate time series classification algorithms.
Study active learning of PTFs with derivative access.
problem Active learning of polynomial threshold functions (PTFs).
method Algorithm for active learning degree-d univariate PTFs with derivative access. result Computational efficient algorithm for active learning degree-d univariate PTFs. Paper proposes CNN-based time series anomaly detection with transfer learning.
problem Time series anomaly detection in automated monitoring systems.
method CNN for segmentation, transfer learning framework, fine-tuning on unseen classes.
result Successfully tested on multiple synthetic and real data sets.
A new multivariate distribution for modeling tails and dependence structures.
problem Modeling tails and dependence structures in multivariate data.
method Generalized Mixed Tempered Stable distribution, random number generation, estimation based on characteristic functions.
result Improved model fitting for multivariate data with better tail behavior and dependence structure.
Improved multivariate time series classification models.
problem Multivariate time series classification challenges.
method Transformed LSTM-FCN and ALSTM-FCN into multivariate models.
result Proposed models outperform state-of-the-art models.
Infers causal direction from mixed-type multivariate data using information theory.
problem Inferring causal direction from multivariate and mixed-type data.
method Information theoretic approach based on Kolmogorov complexity and Minimum Description Length (MDL) principle.
result Crack algorithm reliably infers causal direction with high accuracy.
We show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of systems to be carefully attributed between choices of risk function (e.g. VaR vs CVaR); choice of return distribution (p…
REBMIX package generates, estimates, clusters and classifies multivariate normal mixtures.
problem Generating, estimating, clustering and classifying multivariate normal mixtures with unrestricted variance-covariance matrices.
method Random generation, estimation of components, weights, and parameters, prediction of cluster and class membership.
result Demonstrates the REBMIX package's capabilities for multivariate normal mixtures.
New process capability index for non-normal data.
problem Measuring process capability when data does not follow normal distributions.
method Developed a new multivariate non-parametric PCI using Support Vector Data Description (SVDD).
result Demonstrated improved accuracy in process capability measurement for non-normal data.
Proposes mCS for multivariate selection with FDR control.
problem Selecting high-quality candidates from multivariate datasets.
method Introduces regional monotonicity and multivariate nonconformity scores.
result Significantly improves selection power with FDR control.
Chronos models improve financial forecasting by integrating multivariate data.
problem Improving financial forecasting accuracy using multivariate data.
method Evaluation of Chronos-2 on multivariate and univariate financial forecasting models.
result Multivariate forecasts consistently outperform univariate forecasts, especially for interest rates.
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.
Develops a method for multivariate time series prediction intervals.
problem Uncertainty quantification in multivariate time series forecasting.
method Conformal prediction method for multivariate time series.
result Empirically demonstrates valid coverage of prediction regions.
Neural GARCH models financial time series with time-varying coefficients.
problem Modeling conditional heteroskedasticity in financial time series.
method Neural network adaptation of GARCH and BEKK models with time-varying coefficients parameterized by a recurrent neural network.
result Neural Students t model consistently outperforms other models on financial time series.
Paper forecasts recession indicators using yield spread models.
problem Forecasting the leading indicator of a recession using yield spread.
method Applied econometric time series and machine learning models to forecast yield spread.
result Parsimonious univariate ARIMA model outperforms richly parameterized VAR method.
CATS enhances MTSF by generating ATS from OTS to improve forecasting accuracy.
problem Recent deep learning models often outperform multivariate ones in MTSF.
method CATS constructs ATS from OTS using a 2D temporal-contextual attention mechanism.
result CATS achieves state-of-the-art performance with reduced complexity.