Overview of high-dimensional time series regression methods.
problem Estimation and inference with high-dimensional time series data.
method Limit theory for high-dimensional dependent data, asymptotic theory for time series regression, statistical learning methods.
result Main limit theory results and asymptotic theory for high-dimensional time series regression.
New method for estimating high-dimensional binary time series coefficients.
problem Statistical inference for high-dimensional binary time series.
method Post-selection estimator and second-order wild bootstrap algorithm.
result Good finite-sample performance of the proposed method.
DeepGLO forecasts high-dimensional time series by combining global and local models.
problem Forecasting high-dimensional time series with global patterns and local calibration.
method Hybrid model combining global matrix factorization and local temporal networks.
result DeepGLO outperforms state-of-the-art approaches by more than 25% in WAPE.
A new neural network reduces high-dimensional time-series data for faster classification.
problem Classifying high-dimensional time-series patterns efficiently.
method Developed a time-series discriminant component network (TSDCN) using TSDCA for dimensionality reduction and classification.
result The TSDCN achieves high-accuracy classification and reduces training time.
Paper proposes a new sparsity scheme for high-dimensional VAR models.
problem Estimation of high-dimensional VAR models with sparsity assumptions.
method Regularized estimation procedures for sparse VAR models.
result Threholding extends consistency properties of regularized estimators.
A new VAR model with low-rank constraint for high-dimensional correlated series.
problem Predicting high-dimensional correlated series with hidden factors.
method Vector auto-regressive (VAR) model with low-rank transition matrix.
result Our method shows excellent performances on various simulated datasets and competitive/predictive in real macro-economic data.
DF2M uses deep neural networks within a factor model for high-dimensional functional time series forecasting.
problem Forecasting high-dimensional functional time series with explainability and accuracy.
method Bayesian nonparametric model based on Indian Buffet Process and multi-task Gaussian Process, incorporating a deep kernel function.
result DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models.
A new method for analyzing high-dimensional time-series data using deep neural networks.
problem Challenges in modeling high-dimensional time-series data with explicit state and observation processes.
method Deep Direct Discriminative Decoders (D4) for high-dimensional observation processes.
result D4 outperforms traditional SSMs and RNNs in various time-series data applications.
Model predicts road traffic using high-dimensional time-series with L1-penalization.
problem Predicting high-dimensional road traffic data with limited observations.
method Vector autoregressive model with L1-penalization for high-dimensional regression.
result The approach identifies the most important road sections and is competitive in prediction.
Novel method converts time series data into functional data for high dimensional classification.
problem Small sample size problem in high dimensional time series data.
method Classwise Functional Principal Component Analysis (PCA) followed by Bayesian linear classifier.
result Demonstrated efficacy on synthetic and real data sets.
Paper tackles imbalanced time series classification with a novel oversampling method.
problem Imbalanced time series classification challenges due to high dimensionality and correlation.
method Density-ratio based clustering followed by shrinkage technique for covariance estimation, then generating synthetic samples.
result OHIT outperforms state-of-the-art methods in F1, G-mean, and AUC metrics.
High-dimensional time series prediction is needed in applications as diverse as demand forecasting and climatology. Often, such applications require methods that are both highly scalable, and deal with noisy data in terms of corruptions or missing values. Classical time series methods usually fall short of handling bot…
Study high-dimensional Granger causality tests for VIX and financial news.
problem Testing Granger causality in high-dimensional time series data.
method Regularized regressions, sparse-group LASSO, HAC estimation of variance.
result Valid time series inference for Granger causality tests in high dimensions.
A framework for forecasting high-dimensional time-series data using clustering.
problem Forecasting high-dimensional time-series data with intra-cluster similarity.
method Three-stage framework: univariate time series parameter estimation, clustering, multivariate time series parameter computation.
result Framework achieves state-of-the-art results on benchmark datasets, sometimes outperforming deep-learning-based approaches.
Paper detects and estimates breaks in high-dimensional functional time series.
problem Detecting and estimating structural breaks in heterogeneous mean functions of high-dimensional functional time series.
method Proposes a new test statistic combining functional CUSUM and power enhancement components, with a clustering algorithm for group structure estimation.
result The proposed techniques have satisfactory performance in finite samples, detecting and estimating breaks effectively.
The paper introduces a method for interpretable principal component analysis of high-dimensional time series.
problem Inconsistent and difficult-to-interpret principal component estimates in high-dimensional regimes.
method Localized sparse principal component analysis of spectral density matrices in frequency domain.
result Efficient algorithm for sparse-localized estimates of principal subspaces.
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.
Proposes a method to forecast dependencies between thousands of time series.
problem Computational and numerical difficulties in estimating high-dimensional covariance matrices.
method Combines RNN and Gaussian copula process with low-rank covariance structure.
result Significant accuracy improvements over state-of-the-art baselines.
Sparse graph learning for dependent time series using ADMM.
problem Inferring conditional independence graph of sparse, high-dimensional stationary multivariate Gaussian time series.
method Sparse-group lasso-based frequency-domain formulation and alternating direction method of multipliers (ADMM) optimization.
result Convergence of inverse PSD estimators to true value under certain conditions.
Paper proposes a new sparse VAR model for high-dimensional time series.
problem Non-identifiability, computational intractability, and difficulty of interpretation for high-dimensional time series.
method Sparse infinite-order VAR model with ℓ1-regularized estimation methods. result Greater statistical efficiency and interpretability achieved with little loss of temporal information.
New deep probabilistic model handles missing data in time series forecasting.
problem Handling missing data in time series forecasting.
method Combination of deep learning and probabilistic methods.
result Advantage in forecasting and novelty detection with missing data.
A new SVM method for predicting time series labels.
problem Learning to predict labels from high-dimensional time series data.
method Extended SVM concept to continuous time series data, formulated as a convex optimization problem.
result Empirical results show the algorithm's effectiveness for analyzing long-term multivariate data.
A new method identifies critical transitions in high-dimensional data.
problem Challenges in identifying critical transitions in high-dimensional time-series data.
method Spatial-temporal Principal Component Analysis (stPCA)
result Identifies tipping points before critical transitions reliably.
Proposes an EM algorithm for high-dimensional Markov-switching VAR models.
problem Estimating regime shifts in high-dimensional time series data.
method Approximate EM algorithm for Markov-switching VAR models.
result Established consistency of the proposed EM algorithm in high dimensions.
DeepLINK-T uses deep learning and knockoffs for time series data.
problem Interpreting and reproducible deep learning models for high-dimensional time series data.
method Combines deep learning with knockoffs for FDR control in feature selection for time series models.
result DeepLINK-T effectively controls FDR while demonstrating superior feature selection for high-dimensional longitudinal time series data.
Method learns latent SDEs from high-dimensional time series.
problem Learning latent stochastic differential equations from time series data.
method Self-supervised learning with variational autoencoders and Euler-Maruyama approximation.
result Can recover SDE coefficients and latent variables up to isometry with infinite data.
Approach selects variables and time intervals for comparing high-dimensional time-series data.
problem Comparing high-dimensional time-series data for significant differences.
method Data is split into subintervals, and two-sample tests are performed on each to identify distinguishing variables.
result The approach effectively identifies variables and time intervals where data significantly differs.
Novel estimation methods improve MAR model accuracy for high-dimensional time series.
problem Limited estimation techniques for Matrix Autoregressive (MAR) models.
method Adapted Yule-Walker equations and Burg's method.
result Proposed methods achieve comparable model fit to VAR models.
Improves change-point detection for high-dimensional time-series.
problem Uncertainty in latent variable estimation affects change-point detection.
method Proposes multinomial sampling to improve detection rate and reduce delay.
result Results outperform baseline method in experiments.
Anomaly detection for high-dimensional data using large deviations principle.
problem Challenges in anomaly detection for high-dimensional data.
method Large Deviations Anomaly Detection (LAD) algorithm.
result Outperforms state-of-the-art methods on high-dimensional data sets.
Enhances VAR model estimation using transfer learning.
problem Estimating high-dimensional VAR models with temporal dependencies.
method Transfer learning for VAR models with low-rank and sparse structures.
result Theoretical guarantees for model parameter consistency and informative set selection.
A new framework for time series analysis using state-space learning.
problem Ineffectiveness of traditional Kalman filtering in handling big data and multiple explanatory variables.
method State Space Learning (SSL) framework using statistical learning for high-dimensional regression.
result SSL outperforms traditional methods in subset selection and forecasting accuracy.
CP-factorization for high-dimensional tensor time series and double projection iterations
problem Identifying and estimating factor loadings in CP decomposition for high-dimensional tensor time series
method One-pass estimation procedure using standard eigen-analysis for matrix constructed based on serial dependence
result Asymptotic properties established under general settings, adapt to sparsity, accommodates weak factors
ReGEN-TAD detects anomalies in financial time series with interpretable models.
problem Detecting anomalies in complex financial time series with high-dimensional data.
method Integrates machine learning with econometric diagnostics in a refined convolutional--transformer architecture.
result Unified anomaly score without labeled data, robust to structured deviations.
Develops a method to estimate network difference in high-dimensional time series data.
problem Estimating network differences in high-dimensional data can be unreliable.
method Uses an L1 penalty on the difference of inverse spectral densities to estimate network differences.
result Establishes consistency of the method for sparse network differences.
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension d to scale with the series length T. We treat the transition matrix of time series as a nuisance parameter and directly apply sparse principal component…
A new method scales Gaussian process variational autoencoders to handle high-dimensional time series.
problem Scalability issue in Gaussian process variational autoencoders (GPVAEs).
method Introducing Markovian GPs and using Kalman filtering and smoothing for linear time training.
result MGPVAE outperforms existing approaches in various tasks with high scalability.
Numerous control and learning problems face the situation where sequences of high-dimensional highly dependent data are available but no or little feedback is provided to the learner, which makes any inference rather challenging. To address this challenge, we formulate the following problem. Given a series of observati…
Bayesian method for high-dimensional VECM analysis of cointegration.
problem Efficiently determining cointegration rank in high-dimensional time series.
method Bayesian approach to analyze cointegration matrix.
result Promising results in high-dimensional settings with low sample size.
Challenge hides and seeks privacy in clinical time-series data.
problem De-identifying clinical time-series data while preserving utility and privacy.
method Synthetic data generation to preserve temporal dynamics and limit re-identification risk.
result A novel competition tracks synthetic data generation and patient re-identification.
High-dimensional inference for sparse spectral precision matrices
problem Inference on the spectral precision matrix at a fixed frequency
method Full likelihood-based inference using neighboring discrete Fourier transforms
result Simultaneous control of regularization, finite-sample truncation, and smoothing biases
Granger causality reviewed and advanced for complex data.
problem Validity of inferring causal relationships from time series data.
method Recent advances in models for high-dimensional time series, accounting for nonlinear and non-Gaussian observations, and sub-sampled data.
result Improved computational tools for Granger causality.
A distributed framework for reducing high-dimensional matrix-variate time series data.
problem Reducing dimensionality of high-dimensional, heterogeneous matrix-variate time series data.
method Data partitioning, distributed two-dimensional tensor PCA, aggregation, final PCA, factor matrix computation.
result Preserves latent matrix structure, improves computational efficiency and information utilization.
Proposes a model for classifying high-dimensional time series with interpretable parameters.
problem Challenges in classifying high-dimensional time series, especially in neuroscience.
method Model-based approach using sparsity in inverse spectral density matrices, with interpretability of model parameters.
result Model demonstrates consistency and sure screening property, enabling nuanced inferences.
Estimates change point in high dimensional time series models.
problem Change point estimation in high dimensional time series.
method Plug-in least squares estimator with sufficient conditions for adaptivity.
result Optimal rate of convergence Op(ξ−2) in integer scale. ProFnet models HDFTS with neural networks, offering scalable probabilistic forecasts.
problem Modeling high-dimensional functional time series with nonlinear trends and high spatial dimensions.
method Integrates feedforward and deep neural networks with probabilistic modeling.
result Superior performance in forecasting Japan's mortality rates.
Proposes online debiasing to correct bias in adaptive data collection for high-dimensional linear regression.
problem Bias in adaptive data collection for high-dimensional linear regression.
method Online debiasing procedure for LASSO and other estimators.
result Optimal debiasing of LASSO estimator in specific sparsity regime.
Proposes a deep learning model for imputing missing values in time series data.
problem Missing values in multivariate time series data.
method Deep sequential latent variable model using Gaussian process and VAE.
result Outperforms classical and deep learning methods in imputation accuracy and interpretability.