Consistent estimation of constrained autoregressive processes.
problem Estimating autoregressive processes with coefficients constrained to an ellipsoid.
method Use of constrained and penalized estimators under different norms.
result Provide consistency results for estimation of constrained autoregressive processes.
Speculation leads to power law price changes.
problem Financial price changes follow a power law distribution.
method Identified speculation as the underlying mechanism.
result Price changes follow a random coefficient autoregressive process.
New method for identifying graph shift operators using vertex-time autoregressive models.
problem Identifying graph shift operators from graph signals.
method Online optimization using vertex-time autoregressive model and stochastic gradient projection.
result Successful recovery of graph shift operators from graph signals.
ARMA nets expand receptive fields for dense prediction tasks.
problem Global information in dense prediction problems is challenging for traditional convolutional layers.
method ARMA layers with adjustable autoregressive coefficients replace traditional convolutions.
result ARMA networks improve dense prediction tasks including video prediction and semantic segmentation.
In this work, we consider the class of multi-state autoregressive processes that can be used to model non-stationary time-series of interest. In order to capture different autoregressive (AR) states underlying an observed time series, it is crucial to select the appropriate number of states. We propose a new model sele…
We propose a new class of models specifically tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series eco…
Paper extracts features from time series to improve forecasting accuracy.
problem Forecasting time series generated by Itô-type processes with unknown coefficients.
method Statistical adjustment of mixture-type models to extract features from time series data.
result Additional statistical features enhance time series prediction accuracy.
The paper revisits classical competition theory to explain speculative asset price dynamics.
problem Understanding the dynamics of speculative asset prices and their volatility.
method Specialized classical model of competition with reservation prices, incorporating speculation.
result The model explains excess, fat-tailed, and clustered volatility in speculative asset prices.
Improves autoregressive models for better density estimation.
problem Limited flexibility of autoregressive models.
method Stacked autoregressive models with random number modeling.
result Achieves state-of-the-art performance in density estimation.
Bayesian method for multivariate autoregressive models with exogenous inputs.
problem Estimating uncertainties in autoregressive models with exogenous inputs.
method Recursive Bayesian estimation via message passing in a factor graph.
result Produces full posterior distributions for autoregressive coefficients and noise precision.
This work proposes an efficient autoregressive model for text generation.
problem The challenge of generating high-quality text with autoregressive models.
method Introduces a cascaded decoding approach using Markov transformers to achieve sub-linear parallel time generation.
result Shows competitive accuracy/speed tradeoff compared to existing methods on five machine translation datasets.
Study shows how insurance processes converge to a specific model for better ruin probability calculations.
problem Calculating ruin probabilities in insurance processes.
method Proved convergence of insurance processes to a generalized Ornstein-Uhlenbeck process, derived approximations for ruin probabilities.
result Discrete-time insurance surplus processes converge weakly to a generalized Ornstein-Uhlenbeck process, providing insights for ruin theory.
Efficiently improves non-autoregressive sequence models for better translation performance.
problem Heavy inference latency and inconsistent output sentences in non-autoregressive models.
method Incorporates a structured inference module with an efficient CRF approximation and dynamic transition technique.
result Significantly better translation performance (BLEU score 26.80) compared to previous non-autoregressive models.
Paper proposes a neural network method for fast, interpretable AR model estimation.
problem Computational inefficiency and convergence issues in conventional AR model estimation.
method Embeds autoregressive structure into a feedforward neural network for coefficient estimation via backpropagation.
result Neural network method consistently recovers AR model coefficients, converging in all cases and providing reliable estimates.
CAESar improves risk forecasting by combining VaR and ES estimates.
problem Lack of tail risk measures in financial risk management.
method Conditional Autoregressive Expected Shortfall model, combining VaR and ES estimates.
result CAESar outperforms existing methods in risk forecasting.
Optimized variable orderings improve autoregressive model performance.
problem Challenges in variable ordering affect autoregressive model efficiency.
method Learn graphical model structure to inform optimal variable orderings.
result Graph-informed orderings yield higher-fidelity samples.
We derive an explicit formula for likelihood function for Gaussian VARMA model conditioned on initial observables where the moving-average (MA) coefficients are scalar. For fixed MA coefficients the likelihood function is optimized in the autoregressive variables Φ's by a closed form formula generalizing regression c…
Theoretical study of random forests for nonlinear time series.
problem Theoretical justification for using random forests in time series modeling.
method Uniform concentration inequality for regression trees and random forests consistency proof.
result Consistency of random forests for nonlinear autoregressive processes.
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.
New method estimates spin system mutual information using neural networks.
problem Estimating mutual information in spin systems.
method Monte Carlo sampling enhanced by autoregressive neural networks.
result Area law satisfied for temperatures away from critical temperature.
The paper develops bootstrap methods for ACD models with random durations.
problem Bootstrap inference for autoregressive duration models with random durations.
method Recursive schemes for fixed calendar span or realized event count.
result The bootstrap method reproduces the conditional Gaussian component for ACD models with 0<κ<1. Paper proposes forecast-necessity testing for accurate causal interpretation in nonlinear time-series models.
problem Misinterpretation of causal scores from nonlinear models as regression coefficients.
method Systematic edge ablation and forecast comparison to evaluate causal necessity.
result Causal relationships with similar scores can differ in their necessity for accurate prediction.
Two algorithms improve fitting autoregressive models for big data.
problem Efficiently solving Toeplitz least squares problems for large time series data.
method Applied randomized numerical linear algebra (RandNLA) techniques.
result LSAR algorithm is more robust for real-world time series data.
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.
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 …
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.
A new model uses normalizing flows for discrete sequences, improving generation speed.
problem Modeling discrete sequences like text using normalizing flows poses challenges.
method Proposes a VAE-based model with autoregressive and non-autoregressive flow architectures.
result Flow-based models can match or improve on autoregressive baselines for discrete sequence tasks.
A new method for filling in missing traffic data improves accuracy over existing techniques.
problem Incomplete spatiotemporal traffic data.
method Low-rank autoregressive tensor completion (LATC) framework.
result LATC framework better captures spatiotemporal consistency and local consistency.
New model handles missing data effectively in autoregressive models.
problem Handling missing data in autoregressive models.
method Reinterpret existing models through missing data lens, introduce principled framework for incomplete datasets, active information acquisition.
result MO-ARM consistently outperforms imputation baselines across real-world benchmarks.
The paper solves MMV and MV problems with random coefficients and finds shared optimal strategies.
problem Optimal trading strategies with random market coefficients.
method Backward stochastic differential equations (BSDEs) to find optimal strategies.
result MMV and MV problems share the same optimal portfolio and value under random coefficients.
New conditions for ACD model consistency and normality.
problem Random number of durations in ACD model.
method Additional sufficient conditions for consistency and normality of QMLE.
result Finite mean of durations is required for consistency and normality.
We introduce the Randomized Dependence Coefficient (RDC), a measure of non-linear dependence between random variables of arbitrary dimension based on the Hirschfeld-Gebelein-Rényi Maximum Correlation Coefficient. RDC is defined in terms of correlation of random non-linear copula projections; it is invariant with respec…
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.
Paper evaluates AR model-based methods for time series prediction with missing data.
problem Predicting time series with missing values in real-world applications.
method Adapted five mainstream methods for AR-model-based online time series prediction with missing values.
result Imputation is a simple but effective strategy for handling missing values in online prediction tasks.
Paper proposes a forecasting model combining autoregressive models with spectral attention.
problem Time series forecasting across various domains.
method Combines deep autoregressive models with Spectral Attention (SA) module.
result SAAM consistently demonstrates improved forecasting accuracy compared to state-of-the-art approaches.
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.
A new autoregressive SPO method improves decision-making for dependent data.
problem Improving decision-making for dependent data in stochastic optimization.
method An autoregressive Smart Predict-then-Optimize (SPO) method for time series data.
result Generalization bounds and uniform calibration results for the SPO loss in autoregressive models.
AR-Sieve Bootstrap improves Random Forest time series prediction accuracy.
problem Inaccurate time series prediction due to inadequate resampling methods.
method Combines Random Forest with AR-Sieve Bootstrap for better resampling.
result AR-Sieve Bootstrap leads to more accurate predictions compared to other methods.
Paper proposes methods for transfer learning with random coefficient ridge regression.
problem Estimation and prediction in high-dimensional settings with related models.
method Two estimators using weighted sums of ridge estimates from target and source models.
result Explicit expression of estimation and prediction risks derived using random matrix theory.
In this work, we study the problem of aggregating a finite number of predictors for nonstationary sub-linear processes. We provide oracle inequalities relying essentially on three ingredients: (1) a uniform bound of the ℓ1 norm of the time varying sub-linear coefficients, (2) a Lipschitz assumption on the predict…
Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective time-lagged instances. As in most cases a parsimonious causality structure is assumed, a promising approach to causal discov…
Study optimal consumption and investment strategies with constraints in a market with random coefficients.
problem Optimal consumption and investment strategies with constraints in a regime switching market with random coefficients.
method Explicit optimal strategies provided via solutions to new BSDE systems.
result Solving new BSDEs to find optimal values and strategies.
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.
We introduce BAR processes for modeling binary interactions and show they mix rapidly.
problem Modeling binary interactions in various graphical structures.
method Introduce Bernoulli Autoregressive Processes (BAR) with autoregressive dynamics and efficient structure learning.
result BAR processes mix rapidly with a mixing time of O(logp). Study optimal portfolios for many players in a market model with random coefficients.
problem Optimal portfolio selection for many players under relative performance criteria in a market model with random coefficients.
method Game theory and stochastic optimal control, focusing on CARA and CRRA risk preferences, and extending to continuum of players.
result Existence of forward Nash equilibrium and mean field equilibrium for the n-agent game and corresponding mean field stochastic optimal control problem.
Using a proper model to characterize a time series is crucial in making accurate predictions. In this work we use time-varying autoregressive process (TVAR) to describe non-stationary time series and model it as a mixture of multiple stable autoregressive (AR) processes. We introduce a new model selection technique bas…
Study tracks cryptocurrency news on social media.
problem Real-time tracking of relevant cryptocurrency news.
method Matched web news with social media tweets, analyzed tweet activity, and used machine learning models.
result Random forest autoregressive model performs well in predicting article mentions.