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.
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arXiv research
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.
169,341 papers · 148 categories
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48 results for “Univariate”
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.
This paper improves multi-label ranking by reweighting univariate losses, enhancing consistency and performance.
problem Improving multi-label ranking performance while maintaining consistency.
method Systematic study of consistency and generalization error bounds for learning algorithms, proposing a reweighted univariate loss.
result Inconsistent pairwise losses can lead to better performance than consistent univariate losses in practice.
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.
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.
Study assesses drought and late-frost risks in Bavaria using vine copulas.
problem Assessing risks of late-frost and drought in Bavaria due to climate change.
method Used vine copula models for non-Gaussian and asymmetric dependencies, with univariate and bivariate regression analyses.
result Identified 'at-risk' regions for forest adaptation.
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.
Recent advances in statistical theory, together with advances in the computational power of computers, provide alternative methods to do mass-univariate hypothesis testing in which a large number of univariate tests, can be properly used to compare MEEG data at a large number of time-frequency points and scalp location…
Deep learning methods improve time series forecasting by optimizing lag selection.
problem Optimizing the number of lags for accurate univariate time series forecasting.
method Empirical analysis of deep learning methods trained on multiple time series datasets.
result Excessively small or large lag sizes negatively impact forecasting performance.
Optimal unimodal fitting for linear loss functions in a sequential, efficient manner.
problem Optimal unimodal transformation of univariate model scores under linear loss functions.
method Proposes a sequential approach to estimate the optimal rectangular fit for observed samples with each new sample.
result Sequential approach achieves optimal efficiency with logarithmic time complexity per iteration.
Statistical tests that compare classification algorithms are univariate and use a single performance measure, e.g., misclassification error, 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…
Develops robust classification algorithms for positive-unlabeled data with noisy labels.
problem Learning from positive-unlabeled data with noise in positive labels.
method Explicitly models noise in positive labels and uses univariate transforms built on discriminative classifiers.
result Estimates class prior and posterior distributions robustly from noisy positives and unlabeled data.
Proposes a method to partition univariate data into unimodal subsets.
problem Partitioning univariate multimodal data into unimodal subsets.
method Recursive splitting around valley points of the data density using properties of critical points on the convex hull of the ecdf plot.
result Obtains a hierarchical statistical model of the initial dataset as a mixture of UMMs.
Study active learning of PTFs with derivative access.
problem Active learning of polynomial threshold functions (PTFs).
method Algorithm for active learning degree- univariate PTFs with derivative access.
result Computational efficient algorithm for active learning degree- univariate PTFs.
Deep reinforcement learning models win trading games on time series data.
problem Optimizing trading strategies for time series data.
method Deep Q-learning models (GRU, LSTM, CNN, MLP) trained on idealized trading games.
result Models can find profitable trading strategies for both univariate and bivariate time series data.
AI learns to classify and represent univariate distributions in a 2D latent space.
problem Classifying and representing univariate empirical distributions.
method Unsupervised beta variational autoencoder (beta-VAE) to separate and represent distributions in a 2D latent space.
result The latent space representation separates distributions of different shapes while overlapping similar ones.
Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Familiesstat.ML
GRM models k-way dependencies in univariate exponential families.
problem Modeling dependencies between variable sets of size k > 2.
method Taking k-th root of sufficient statistics for univariate exponential families.
result GRM models for Poisson and exponential families have no and only slight restrictions on parameters, respectively.
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecastingstat.ML
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.
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.
Efficient binary sampling method for global optimization of univariate functions with low regret.
problem Global optimization of univariate loss functions.
method Binary sampling approach to circumvent hard-to-determine query points in traditional methods.
result At most and regret for -Lipschitz continuous and -Lipschitz smooth functions respectively.
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.
Shallow neural networks can represent polynomials efficiently.
problem Representing polynomials using shallow neural networks.
method Using shallow neural networks of width to represent -variate polynomials of degree .
result Derives minimax optimal convergence rate for shallow networks to unknown univariate regression functions.
uGMM-NN integrates probabilistic reasoning into neural networks.
problem Capturing multimodality and uncertainty in neural network activations.
method Parameterizes activations as univariate Gaussian mixtures with learnable parameters.
result Competitive discriminative performance with probabilistic activations.
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.
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 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.
The paper offers a unified approach to the study of three locally adaptive estimation methods in the context of univariate time series from both theoretical and empirical points of view. A general procedure for the computation of critical values is given. The underlying model encompasses all distributions from the expo…
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.
Simple anomaly detector adapts to non-stationary data.
problem Detecting anomalies in univariate time-series data.
method Adapts to non-stationarity using conformal prediction.
result Performs similarly to complex models on benchmark datasets.
Trend Filtering on Graphsstat.ML
We introduce a family of adaptive estimators on graphs, based on penalizing the norm of discrete graph differences. This generalizes the idea of trend filtering [Kim et al. (2009), Tibshirani (2014)], used for univariate nonparametric regression, to graphs. Analogous to the univariate case, graph trend filteri…
New neural networks learn distribution functions using quantiles and moments.
problem Approximating functions of distributions in probability spaces.
method Quantile and moment neural networks, mixing quantile and moment features.
result Moment neural network outperforms others for bivariate distributions.
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.
Proposes a sparse linear classifier for classification with pairwise dependencies.
problem Classification accuracy is limited by tree-structured graphical models.
method Semi-parametric approach using sparse linear combination of univariate and bivariate log-transformed densities.
result SLB classifier is competitive with popular methods.
New statistical methods improve explainability of boosting models.
problem Uncertainty quantification for boosting models is computationally intensive and hard to interpret.
method Derive methods for statistical inference using gradient boosting and Boulevard regularization.
result Achieve asymptotically normal predictions with theoretical guarantees and runtime independent of data size.
Directional Kernel Mean Difference: A Fast Signed Statistic for Univariate Distribution Comparisonstat.ML
DKMD is a fast signed statistic for comparing univariate distributions.
problem Comparing univariate distributions, especially preserving directionality.
method DKMD integrates kernel mean embeddings against an odd weighting function.
result DKMD preserves directionality and is robust to outliers.
Paper presents methods to create stock price confidence intervals using LSTM models.
problem Creating accurate confidence intervals for LSTM-estimated stock prices.
method Three bootstrap methods for dependent data, optimal block length selection, and benchmark comparison.
result Illustrated through stock price data, different bootstrap strategies provide varying confidence intervals.
A boosting method improves nonparametric density estimation without smoothing assumptions.
problem Overfitting in nonparametric data fitting.
method Introduces a boosting algorithm for univariate nonparametric maximum likelihood estimation.
result Demonstrates the effectiveness of the boosting approach through simulations and real data experiments.
LSTMs improve bond yield forecasting with unique signals.
problem Improving bond yield forecasting accuracy.
method Long short-term memory (LSTM) networks with sequence-to-sequence architectures and LSTM-LagLasso methodology.
result Univariate LSTM models with additional memory can achieve similar results as multivariate MLP models using exogenous information.
New model predicts time series quantiles for nonstationary data.
problem Nonparametric probabilistic forecasting of nonstationary univariate time series.
method Composite Quantile Fourier Neural Network (QFNN) for extrapolation-based nonlinear quantile regression.
result Effective in providing high quality and accurate probabilistic predictions.
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.
PSF R package automates PSF algorithm for univariate time series forecasting.
problem Univariate time series forecasting challenges.
method Pattern Sequence based Forecasting (PSF) algorithm with clustering and prediction.
result Automated implementation of PSF algorithm simplifies forecasting.
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 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.
Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powerful techniques to sample from almost arbitrary distributions. The flaw in practice is that it can take a large and/or unknown amount of time to converge to the stationary distribution. This paper gives sufficient conditions to guarantee that univa…
This work provides guaranteed bounds on the total variation distance for univariate mixtures.
problem Lack of closed-form expressions for total variation distance between mixtures.
method Two methods: information monotonicity for lower bounds and geometric envelopes for upper bounds.
result Demonstrated tightness of bounds on Gaussian, Gamma, and Rayleigh mixtures.
EM algorithm converges slowly for weakly identifiable Gaussian mixtures.
problem Slow convergence of EM algorithm for weakly identifiable Gaussian mixtures.
method Localized argument with two stages, each involving epoch-based arguments for surrogate EM operators at the population level.
result EM algorithm converges in $n^{ rac{3}{4}}$ steps with estimates at Euclidean distance of $n^{- rac{1}{8}}$ and $n^{- rac{1}{4}}$ from true parameters.
We consider the problem of rank loss minimization in the setting of multilabel classification, which is usually tackled by means of convex surrogate losses defined on pairs of labels. Very recently, this approach was put into question by a negative result showing that commonly used pairwise surrogate losses, such as ex…