A new clustering method for vector time series using autoregressive dynamics.
problem Clustering of vector time series based on their dynamics is challenging.
method System identification approach using mixture autoregressive models.
result Developed a computationally manageable algorithm k-LMVAR for clustering vector time series.
Study improves dividend discount model using VAR process.
problem Improving dividend discount models for better predictions.
method Introduced a Gordon growth model based on Vector Autoregressive Process (VAR).
result Two Propositions related to the new model.
Study LASSO for high-dimensional VAR models with weakly dependent innovations.
problem Understanding sparse regularization in high-dimensional VAR models with weakly dependent innovations.
method LASSO estimation for weakly sparse VAR models with heavy tailed innovations, under L1 mixingale condition. result Oracle properties of LASSO estimation in high-dimensional VAR models with weakly dependent innovations.
We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood …
New framework IIA identifies innovations in general nonlinear vector autoregressive processes.
problem Limited generality of NVAR models due to additive innovation assumption.
method Independent Innovation Analysis (IIA) framework, assuming mutual independence and modulation by an auxiliary variable.
result Guarantees identifiability of innovations with arbitrary nonlinearities, up to permutation and component-wise invertible nonlinearities.
We present a distributionally robust formulation of a stochastic optimization problem for non-i.i.d vector autoregressive data. We use the Wasserstein distance to define robustness in the space of distributions and we show, using duality theory, that the problem is equivalent to a finite convex-concave saddle point pro…
LTMs use latent vectors for efficient autoregressive generation.
problem Efficient autoregressive generation in language models.
method Dual-rate optimization in variational Bayes framework.
result LTMs achieve superior sample and parameter efficiency.
Paper uses non-linear dimension reduction for better economic forecasting.
problem Analyzing economic effects of shocks in large datasets.
method Non-linear dimension reduction in factor-augmented vector autoregressions.
result Non-linear dimension reduction techniques improve forecasting, especially in volatile data.
Neural autoregressive models are explicit density estimators that achieve state-of-the-art likelihoods for generative modeling. The D-dimensional data distribution is factorized into an autoregressive product of one-dimensional conditional distributions according to the chain rule. Data completion is a more involved ta…
Vector autoregressive models characterize a variety of time series in which linear combinations of current and past observations can be used to accurately predict future observations. For instance, each element of an observation vector could correspond to a different node in a network, and the parameters of an autoregr…
The paper uses Bayesian methods to infer hidden processes with unknown parameters.
problem Estimating hidden processes from noisy observations with unknown parameters.
method Variational Bayesian inference with autoregressive moving average (ARMA) and vector autoregressive (VAR) models, combined with sequential Monte Carlo (SMC) and importance sampling resampling (SISR).
result The proposed inference method accurately estimates hidden states from non-linear noisy observations.
Sparse Tucker decomposition with graph regularization improves time series forecasting accuracy.
problem High-dimensional time series forecasting with over-parameterization issue.
method Sparse Tucker decomposition and graph regularization for tensor-based model.
result Non-asymptotic error bound and superior performance in numerical experiments.
BAVART model combines VAR and BART for non-linear forecasting.
problem Overly restrictive linearity assumption in VAR models.
method Combining VAR with Bayesian additive regression trees (BART).
result BAVART model yields highly competitive forecasts.
Linear attention in Transformers can be interpreted as dynamic VAR models.
problem Misalignment between Transformers and autoregressive forecasting objectives.
method Interpreting linear attention as VAR, rearranging MLP, attention, and flow.
result SAMoVAR improves performance, interpretability, and efficiency.
Proposes PredVAR model for reduced-dimensional dynamics from noisy data.
problem Extracting low-dimensional dynamics from high-dimensional noisy data.
method Probabilistic reduced-dimensional vector autoregressive model with oblique projection.
result Iterative algorithm yields dynamic latent variables with rank-ordered predictability.
ARMA cell simplifies neural autoregressive modeling for time series.
problem Complex RNN cells are not always necessary and can be inferior.
method Introduces ARMA cell, a simpler, modular approach for neural time series modeling.
result The ARMA cell is competitive with popular alternatives in performance.
The paper proposes a new model for predicting and analyzing economic variables.
problem Predicting and analyzing economic variables in developed regions.
method Time-varying parameter global vector autoregressive (TVP-GVAR) framework combined with machine learning models.
result The proposed model provides high precision out-of-sample predictions and novel insights into economic variable connectedness.
High-speed model accurately simulates neuromorphic devices.
problem Accurately modeling stochastic synapses in large-scale neuromorphic systems.
method Generative vector autoregressive model based on resistive memory cell data.
result Fast, high-throughput model reproduces synaptic parameters and correlations.
mGENRE improves multilingual entity linking with autoregressive sequence prediction.
problem Multilingual Entity Linking (MEL) task of resolving language-specific mentions to a multilingual Knowledge Base.
method Autoregressive sequence-to-sequence system that cross-encodes mention strings and entity names.
result Over 50% improvement in average accuracy in zero-shot settings.
Incorporating nonlinearity is paramount to predicting the future states of a dynamical system, its response to shocks, and its underlying causal network. However, most existing methods for causality detection and impulse response, such as Vector Autoregression (VAR), assume linearity and are thus unable to capture the …
A new model trains prior and encoder/decoder networks simultaneously for efficient generation.
problem Complex autoregressive prior in VQ-VAE models leads to slow generation.
method Builds a diffusion bridge between continuous and non-informative prior distributions.
result Model is competitive and efficient in optimization and sampling.
New method for estimating and testing impulse responses in high-dimensional VAR systems.
problem Statistical inference for impulse responses in sparse, high-dimensional vector autoregressions.
method Local projection equations and de-sparsified estimators combined with a non-regularized contemporaneous impact matrix.
result Valid inference procedures for structural impulse responses in high-dimensional systems.
High-dimensional time series data exist in numerous areas such as finance, genomics, healthcare, and neuroscience. An unavoidable aspect of all such datasets is missing data, and dealing with this issue has been an important focus in statistics, control, and machine learning. In this work, we consider a high-dimensiona…
Calculates local Granger causality for Gaussian and nonlinear systems.
problem Understanding causal influence in complex systems.
method Vector autoregression and information-theoretic approach.
result Local Granger causality offers a robust and fast method for time-directed information transfer.
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…
Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the p…
The paper develops fast Bayesian methods for estimating huge PVARs with competitive forecasts.
problem Computational and statistical issues in estimating PVARs with many parameters.
method Integrated rotated Gaussian approximations, exploiting domestic over international information, and fast approximations for international coefficients.
result Produces competitive forecasts quickly using a huge world economy model.
Study on estimating sparse transition matrix of partially-observed VAR with noisy and sparse data.
problem Estimating sparse transition matrix of partially-observed VAR with noisy and sparse data.
method Yule-Walker equation, Dantzig selector, minimax lower bound.
result Near-optimality of the proposed estimator with convergence rate analysis.
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.
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singul…
Study shows how market efficiency changes during the pandemic.
problem Understanding market efficiency during the pandemic.
method Applied time-varying vector autoregression model.
result Market efficiency changes over time and can be improved by enhanced linkages.
Proposes a Structural Matrix Autoregressive model for joint analysis of asset returns, realized volatility, and trading volume.
problem Joint analysis of asset returns, realized volatility, and trading volume
method Structural Matrix Autoregressive model
result Volatility is primary driver of trading activity, with informational shocks incorporated through price variability.
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.
The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from …
GENRE retrieves entities autoregressively, improving efficiency and accuracy.
problem Retrieving entities from queries efficiently and accurately.
method Autoregressive generation of entity names, reducing memory footprint and improving context encoding.
result Significantly improved performance on entity disambiguation, linking, and retrieval tasks.
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order of magnitude than the sample …
Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families of approaches that have been shown useful in effective brain connectivity studies. While VARMs postulate that a given region of interest in the brain is directionally connected to another one by virtue of time-lagged influ…
SpinSVAR estimates SVAR models with sparse input, improving accuracy and scalability.
problem Estimating SVAR models with sparse input assumptions.
method SpinSVAR models input as independent Laplacian variables, enforcing sparsity and using least absolute error regression.
result SpinSVAR outperforms state-of-the-art methods in accuracy and runtime, identifying significant structural shocks.
Study examines dynamic relationship between BRICS stocks and cryptocurrencies.
problem Understanding the impact of BRICS stock markets on cryptocurrency markets.
method Time-varying parameter vector autoregression model (TVP-VAR).
result Three out of five BRICS stock markets are primary sources of shocks affecting the financial network.
Paper proposes a new MAR model for global economic forecasting.
problem Joint modeling of economic and financial variables across countries.
method Sparse matrix autoregressive model with trade network integration.
result Sparse component differentiates systematic and idiosyncratic cross-predictability.
New algorithms solve linear algebra problems in sublinear time.
problem Numerical linear algebra problems, especially with structured matrices.
method Sublinear time algorithms using matrix-vector multiplications.
result Solve problems like least squares regression and low rank approximation in sublinear time.
Bayesian VAR model discovers Granger causality with uncertainty-aware binary graphs.
problem Discovering Granger causal relations from multivariate time-series data.
method Bayesian Vector AutoRegression with factorised Granger-Causal Graphs.
result Our method achieves better performance, especially in low-data regimes.
In the paper, we consider the problem of link prediction in time-evolving graphs. We assume that certain graph features, such as the node degree, follow a vector autoregressive (VAR) model and we propose to use this information to improve the accuracy of prediction. Our strategy involves a joint optimization procedure …
Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a surge in interest in discrete latent variable models, however, despite several recent improvements, the training of discrete latent variable m…
The Vector AutoRegressive (VAR) model is fundamental to the study of multivariate time series. Although VAR models are intensively investigated by many researchers, practitioners often show more interest in analyzing VARX models that incorporate the impact of unmodeled exogenous variables (X) into the VAR. However, sin…
VQ-DRAW compresses images and generates realistic samples.
problem Learning compact discrete representations of images.
method Sequential discrete VAE with vector quantization.
result VQ-DRAW effectively compresses and generates images.
New method for inferring network topology from partial data.
problem Inferring network topology from limited node data.
method Vector autoregressive model and Gaussian mixture algorithm.
result The proposed method converges to the network combination matrix in probability.
In this work we consider the problem of anomaly detection in heterogeneous, multivariate, variable-length time series datasets. Our focus is on the aviation safety domain, where data objects are flights and time series are sensor readings and pilot switches. In this context the goal is to detect anomalous flight segmen…