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.
Optimizes Gaussian process hyperparameters using Bayesian autoregression.
Bayesian method estimates Kronecker graphical models from autoregressive processes.
We develop methods to estimate lag and parameters for multiple stable autoregressive processes.
Study improves dividend discount model using VAR process.
Linear autoregressive models serve as basic representations of discrete time stochastic processes. Different attempts have been made to provide non-linear versions of the basic autoregressive process, including different versions based on kernel methods. Motivated by the powerful framework of Hilbert space embeddings o…
New method for identifying autoregressive systems on manifolds.
Model nonstationary spatial processes using normalizing flows.
EventFlow forecasts event sequences without autoregression, improving accuracy.
We consider stationary autoregressive processes with coefficients restricted to an ellipsoid, which includes autoregressive processes with absolutely summable coefficients. We provide consistency results under different norms for the estimation of such processes using constrained and penalized estimators. As an applica…
A new online learning setting for autoregressive processes with sublinear regret.
Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.
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 …
Efficiently combines autoregressive and set-based models for joint distributions.
Novel F2NARX model improves surrogate modeling for stochastic dynamical systems.
Latent Block-Diffusion Temporal Point Processes (LBDTPP) is a semi-autoregressive framework for generating asynchronous event sequences.
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…
The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin-Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic n…
Modified asymmetric hidden Markov models for time series with autoregressive components.
In this article, we first propose the modified Hannan-Rissanen Method for estimating the parameters of the autoregressive moving average (ARMA) process with symmetric stable noise and symmetric stable generalized autoregressive conditional heteroskedastic (GARCH) noise. Next, we propose the modified empirical character…
This paper presents the R package GAS for the analysis of time series under the Generalized Autoregressive Score (GAS) framework of Creal et al. (2013) and Harvey (2013). The distinctive feature of the GAS approach is the use of the score function as the driver of time-variation in the parameters of nonlinear models. T…
We prove that a large class of discrete-time insurance surplus processes converge weakly to a generalized Ornstein-Uhlenbeck process, under a suitable re-normalization and when the time-step goes to 0. Motivated by ruin theory, we use this result to obtain approximations for the moments, the ultimate ruin probability a…
ARCNPs improve CNPs by autoregressively modeling dependencies.
Paper proposes a self-supervised method to denoise autoregressive signals with heavy-tailed noise.
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very general family of graphs, characterised by a variable topology, and attributes associated with nodes and edges. A graph neural network (GNN) …
Alternative sampling method for autoregressive models using Langevin dynamics.
Proposes a non-autoregressive Transformer for time series forecasting.
We consider the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedasticity process, denoted by FIEGARCH(p,d,q), introduced by Bollerslev and Mikkelsen (1996). We present a simulated study regarding the estimation of the risk measure on FIEGARCH processes. We consider the distr…
This paper explores how Transformers predict next tokens in autoregressive tasks.
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…
Improved language generation with faster sampling speed.
A new method for separating mixed signals in space and time.
DiAMoNDBack models protein backmapping from coarse-grained Cα traces.
Efficient methods for answering complex probabilistic queries in sequential data.
New method for identifying graph shift operators using vertex-time autoregressive models.
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…
Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher a…
Discriminator guidance improves autoregressive diffusion models for generating molecular graphs.
Linear attention in Transformers can be interpreted as dynamic VAR models.
Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.
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…
New framework IIA identifies innovations in general nonlinear vector autoregressive processes.
The Multiplicative Error Model (Engle (2002)) for nonnegative valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with nonnegative support. A multivariate extension allows for the innovations to be contemporaneously correlated. We overcome the lack of …
Transformers learn a mesa-optimizer to implement in-context learning.
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…
New GLS estimator handles high-dimensional data with autocorrelated errors.
CAESar improves risk forecasting by combining VaR and ES estimates.
Improved lattice field theory simulations with local-Autoregressive Conditional Normalizing Flow.
We prove the existence of limiting distributions for a large class of Markov chains on a general state space in a random environment. We assume suitable versions of the standard drift and minorization conditions. In particular, the system dynamics should be contractive on the average with respect to the Lyapunov functi…