arXiv research

Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.

Proposes adjusting neural network errors for time series forecasting.

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

Improved deep probabilistic time series forecasting by learning error autocorrelation.

This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker), at economic sectors level of the NUTs III of mainland Portugal, from 1995 to 1999 and from 2000 to 2005 (taking in count the data availability and the Portuguese and European context), c…

In this paper we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving-average (SARMA) models under the assumption that the errors are uncorrelated but not necessarily independent.We relax the standard independence assumption on the error term in order to extend the range …

The consideration of spatial effects at a regional level is becoming increasingly frequent and the work of Anselin (1988), among others, has contributed to this. This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker) in the NUTs III economic …

This study analyses, through cross-section estimation methods, the influence of spatial effects in the conditional product convergence in the parishes' economies of mainland Portugal between 1991 and 2001 (the last year with data available for this spatial disaggregation level). To analyse the data, Moran's I statistic…

This study analyses, through cross-section estimation methods, the influence of spatial effects and human capital in the conditional productivity convergence (product per worker) in the economic sectors of NUTs III of mainland Portugal between 1995 and 2002. To analyse the data, Moran's I statistics is considered, and …

In this paper we propose new algorithm to reduce autocorrelation in Markov chain Monte-Carlo algorithms for euclidean field theories on the lattice. Our proposing algorithm is the Hybrid Monte-Carlo algorithm (HMC) with restricted Boltzmann machine. We examine the validity of the algorithm by employing the phi-fourth t…

Paper proposes a method to break symmetries in Bayesian matrix factorization.

The paper examines how market trade values and volumes affect price autocorrelation.

This paper speeds up Gaussian process regression for autocorrelated data.

This paper reviews deep time-series forecasting focusing on autocorrelation modeling.

Multifractal processes are a relatively new tool of stock market analysis. Their power lies in the ability to take multiple orders of autocorrelations into account explicitly. In the first part of the paper we discuss the framework of the Lux model and refine the underlying phenomenological picture. We also give a proc…

The paper uncovers the impact of price and payoff autocorrelations in multi-period asset pricing models.

FreDF improves forecasting by learning in the frequency domain.

This paper examines autocorrelation in major crypto markets, finding persistent correlations on short time frames.

Financial time series exhibit a number of interesting properties that are difficult to explain with simple models. These properties include fat-tails in the distribution of price fluctuations (or returns) that are slowly removed at longer timescales, strong autocorrelations in absolute returns but zero autocorrelation …

Proposes QDF to improve multi-step time-series forecasting.

In this paper, we study the prediction of a circularly symmetric zero-mean stationary Gaussian process from a window of observations consisting of finitely many samples. This is a prevalent problem in a wide range of applications in communication theory and signal processing. Due to stationarity, when the autocorrelati…

Novel method discovers causal relations in time series data, even with autocorrelation.

The paper calculates optimal trading turnover in terms of asset liquidity and alpha autocorrelation.

Bayesian framework selects features and lags for time series forecasting.

Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees have been recently proposed. This paper extends these robust portfo…

Estimates price elasticity from autocorrelated time series using causal graphs.

This paper analyzes switchback experiments in A/B testing, revealing key factors affecting their effectiveness.

Unified strategy for efficient data compression and model estimation.

Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.

Algorithm constructs coresets for clustering time series data from Gaussian mixtures.

A new RL framework handles autocorrelated actions for better learning and stability.

Introduces a new Hawkes model with CARMA(p,q) intensity to better model dependence structures.

Study of autocorrelation times in neural MCMC simulations for the 2D Ising model.

New method improves causal discovery in time series with latent confounders.

Optimizes portfolio with two controls to minimize trades and maintain signal integrity.

As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the timescale of the latent space while inferring a reduced coordinate, which assists in finding slow processes as according t…

The paper introduces a method to model error correlations in multivariate time series forecasting.

Paper optimizes trend-following portfolios using autocorrelation models.

In this manuscript we analyse the leading statistical properties of fluctuations of (log) 3-month US Treasury bill quotation in the secondary market, namely: probability density function, autocorrelation, absolute values autocorrelation, and absolute values persistency. We verify that this financial instrument, in spit…

Financial market dynamics is rigorously studied via the exact generalized Langevin equation. Assuming market Brownian self-similarity, the market return rate memory and autocorrelation functions are derived, which exhibit an oscillatory-decaying behavior with a long-time tail, similar to empirical observations. Individ…

Paper introduces a new cost function to improve deep learning model generalization.

Study non-local isoperimetric energies on spheres using a Riemannian autocorrelation function.

We describe the impact of the intra-day activity pattern on the autocorrelation function estimator. We obtain an exact formula relating estimators of the autocorrelation functions of non-stationary process to its stationary counterpart. Hence, we proved that the day seasonality of inter-transaction times extends the me…

This work considers the problem of modified portmanteau tests for testing the adequacy of FARIMA models under the assumption that the errors are uncorrelated but not necessarily independent (i.e. weak FARIMA). We first study the joint distribution of the least squares estimator and the noise empirical autocovariances. …

Continuous Hidden Markov Models for Equity Returns

We develop a framework especially suited to the autocorrelation properties observed in financial times series, by borrowing from the physical picture of turbulence. The success of our approach as applied to high frequency foreign exchange data is demonstrated by the overlap of the curves in Figure (1), since we are abl…

Paper tackles temporal overfitting in wind power curve modeling.

We present an original and novel method based on random matrix approach that enables to distinguish the respective role of temporal autocorrelations inside given time series and cross correlations between various time series. The proposed algorithm is based on properties of Wigner eigenspectrum of random matrices inste…