arXiv research

Novel estimation methods improve MAR model accuracy for high-dimensional time series.

Proposes a Structural Matrix Autoregressive model for joint analysis of asset returns, realized volatility, and trading volume.

Paper proposes a new MAR model for global economic forecasting.

Enhanced EEG classification using augmented covariance matrix.

BiGG model efficiently generates sparse graphs with reduced complexity.

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 …

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…

Tensor networks improve unsupervised learning performance.

A new multivariate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix, for the estimation of which a two step procedure is adopted. The first step is the conditional inference on the autoregressi…

FLANDERS detects and blocks extreme model poisoning in federated learning.

Study on estimating sparse transition matrix of partially-observed VAR with noisy and sparse data.

Paper unifies subspace identification and DMD for dynamical systems.

Sparse Tucker decomposition with graph regularization improves time series forecasting accuracy.

New method for identifying graph shift operators using vertex-time autoregressive models.

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…

Paper proposes LATC for multivariate time series prediction and missing data imputation.

New method for inferring network topology from partial data.

Improved covariance matrix forecasting for S&P 500 using factor models and shrinkage.

Transformers become faster by linearizing self-attention.

Tensor networks improve sequence modeling with efficient sampling and parallelism.

We introduce a concept of $autoregressive$ (AR)state-space realization that could be applied to all transfer functions $\boldsymbol{T}(L)$ with $\boldsymbol{T}(0)$ invertible. We show that a theorem of Kalman implies each Vector Autoregressive model (with exogenous variables) has a minimal $AR$-state-space realization …

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…

New GLS estimator handles high-dimensional data with autocorrelated errors.

We study the problem of learning the support of transition matrix between random processes in a Vector Autoregressive (VAR) model from samples when a subset of the processes are latent. It is well known that ignoring the effect of the latent processes may lead to very different estimates of the influences among observe…

Survey of graph learning methods for combinatorial optimization problems.

The vector autoregressive (VAR) model is a powerful tool in modeling complex time series and has been exploited in many fields. However, fitting high dimensional VAR model poses some unique challenges: On one hand, the dimensionality, caused by modeling a large number of time series and higher order autoregressive proc…

Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling th…

A widely applied approach to causal inference from a non-experimental time series $X$, often referred to as "(linear) Granger causal analysis", is to regress present on past and interpret the regression matrix $\hat{B}$ causally. However, if there is an unmeasured time series $Z$ that influences $X$, then this approach…

Estimates spatio-temporal data with satellite NO2 concentrations using Yule-Walker equations.

This work proposes an efficient autoregressive model for text generation.

Autoregressive sequence models achieve state-of-the-art performance in domains like machine translation. However, due to the autoregressive factorization nature, these models suffer from heavy latency during inference. Recently, non-autoregressive sequence models were proposed to reduce the inference time. However, the…

We show how to solve a number of problems in numerical linear algebra, such as least squares regression, $\ell_p$-regression for any $p \geq 1$, low rank approximation, and kernel regression, in time $T(A) \poly(\log(nd))$, where for a given input matrix $A \in \mathbb{R}^{n \times d}$, $T(A)$ is the time needed to com…

Alternative sampling method for autoregressive models using Langevin dynamics.

We propose in this work a new family of kernels for variable-length time series. Our work builds upon the vector autoregressive (VAR) model for multivariate stochastic processes: given a multivariate time series x, we consider the likelihood function p_θ(x) of different parameters θin the VAR model as features to descr…

Bayesian method for multivariate autoregressive models with exogenous inputs.

Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models. However, autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies.…

We present a windowed technique to learn parsimonious time-varying autoregressive models from multivariate timeseries. This unsupervised method uncovers interpretable spatiotemporal structure in data via non-smooth and non-convex optimization. In each time window, we assume the data follow a linear model parameterized …

Paper proposes AXE loss for non-autoregressive machine translation, improving performance.

We introduce a novel multivariate random process producing Bernoulli outputs per dimension, that can possibly formalize binary interactions in various graphical structures and can be used to model opinion dynamics, epidemics, financial and biological time series data, etc. We call this a Bernoulli Autoregressive Proces…

Parallelizes autoregressive generation using VSSM.

FOCUS method forecasts counterfactuals in panel data with time series dynamics.

Linear attention in Transformers can be interpreted as dynamic VAR models.

Efficiently combines autoregressive and set-based models for joint distributions.

Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions. We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which combine an autoregressive component with a log-linear component, allowing the use of global \textit{a…

A new method for filling in missing traffic data improves accuracy over existing techniques.

Paper proposes a self-supervised method to denoise autoregressive signals with heavy-tailed noise.

The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right approach. We introduce a new training method for conditional masked language models, SMART, which mimics the semi-autoregressive behavior of mas…

Paper proposes a new sparsity scheme for high-dimensional VAR models.