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.
We develop methods to estimate lag and parameters for multiple stable autoregressive processes.
The paper explores how mining costs, rewards, and blockchain security are interconnected.
Bayesian framework selects features and lags for time series forecasting.
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series. However, as the number of component series is increased, the VAR model becomes overparameterized. Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation. Traditional a…
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…
New PEMs improve network inference from time-series data.
A new perspective on self-attention models using MLPs.
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…
Estimates spatio-temporal data with satellite NO2 concentrations using Yule-Walker equations.
While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinear. We develop an approach to nonlinear Granger causality detection using multilayer perceptrons where the input to the network is the past t…
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…
Modeling delayed Granger causality in Hawkes processes.
Proposes a method for forecasting time series with multiple seasonality.
We study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. While at shorter microscopic time lags the distribution has power-law tails, for mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an expon…
BAVART model combines VAR and BART for non-linear forecasting.
Study shows oil prices but not COVID-19 cases affect US economic policy uncertainty.
This work is the first part of a project dealing with an in-depth study of effective techniques used in econometrics in order to make accurate forecasts in the concrete framework of one of the major economies of the most productive Italian area, namely the province of Verona. In particular, we develop an approach mainl…
The paper introduces a method to model error correlations in multivariate time series forecasting.
Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Auto…
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…
To reduce the long training time of large deep neural network (DNN) models, distributed synchronous stochastic gradient descent (S-SGD) is commonly used on a cluster of workers. However, the speedup brought by multiple workers is limited by the communication overhead. Two approaches, namely pipelining and gradient spar…
Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and applicable to multi-predictor…
New method speeds up lead-lag detection between asynchronous time series.
Generative model downgrades coarse satellite images to fine resolution.
The present work constitutes the second part of a two-paper project that, in particular, deals with an in-depth study of effective techniques used in econometrics in order to make accurate forecasts in the concrete framework of one of the major economies of the most productive Italian area, namely the province of Veron…
This paper compares ML models for predicting COVID-19 trends.
RNN(p) improves power consumption forecasts with interpretable models.
This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation. Simple rules are designed to detect slowly-varying gradients and, therefore, trigger the reuse of outdated gradients. The resultant gra…
Efficiently combines autoregressive and set-based models for joint distributions.
Enhanced EEG classification using augmented covariance matrix.
New neural network models improve Granger Causality detection in non-linear systems.
Study reduces financial dynamics complexity using PCA for NASDAQ, oil, gold, and USD.
We consider the problem of estimating the parameters of a multivariate Bernoulli process with auto-regressive feedback in the high-dimensional setting where the number of samples available is much less than the number of parameters. This problem arises in learning interconnections of networks of dynamical systems with …
AdaCat improves density estimation and planning in autoregressive models.
Paper proposes a self-supervised method to denoise autoregressive signals with heavy-tailed noise.
Autoregressive models struggle with hard-to-compute distributions, alternatives like energy-based and latent-variable models solve this.
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In this paper, we show that flows can in fact be extended to discrete events---and under a simple change-of-variables formula not requiring log-d…
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…
CARRNN tackles deep learning for sporadic data, improving prediction errors in healthcare.
We propose a generic spatiotemporal event forecasting method, which we developed for the National Institute of Justice's (NIJ) Real-Time Crime Forecasting Challenge. Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian …
This work models financial market returns with asymmetric Tsallis distributions, improving fit over symmetric q-Gaussians.
Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF). We unify a…
Optimized variable orderings improve autoregressive model performance.
Bank transactions help predict macroeconomic indexes faster and more accurately.
Bayesian method for multivariate autoregressive models with exogenous inputs.
HS-FNO models non-Markovian PDEs by learning history and future states.
Transformers encode latent distributions in text, improving performance in out-of-distribution cases.
The study finds a long-term relationship between Dubai crude oil and US natural gas prices.