Search results for "Autocorrelation"

Forecasting time series with missing data using Holt's model

2009

This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holt's model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.

On Independent Component Analysis with Stochastic Volatility Models

2017

Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to…

System size dependence of the autocorrelation time for the Swendsen-Wang Ising model

1990

Abstract We present Monte Carlo simulation results of the autocorrelation time for the Swendsen-Wang method for the simulation of the Ising model. We have calculated the exponential and the integrated autocorrelation time at the critical point T c of the two-dimensional Ising model. Our results indicate that both autocorrelation times depend logarithmically on the linear system size L instead of a power law. The simulations were carried out on the parallel computer of the condensed matter theory group at the University of Mainz.

Bayesian joint modeling for assessing the progression of chronic kidney disease in children.

2016

Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate.

2019

In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA-GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coef…

Multicanonical Monte Carlo simulations

1998

Canonical Monte Carlo simulations of disordered systems like spin glasses and systems undergoing first-order phase transitions are severely hampered by rare event states which lead to exponentially diverging autocorrelation times with increasing system size and hence to exponentially large statistical errors. One possibility to overcome this problem is the multicanonical reweighting method. Using standard local update algorithms it could be demonstrated that the dependence of autocorrelation times on the system size V is well described by a less divergent power law, τ∝Vα, with 1<α<3, depending on the system. After a brief review of the basic ideas, combinations of multicanonical reweighting…

Dynamical susceptibility from simulations of a mean field Potts glass

2004

Abstract We present results of the non-linear dynamic susceptibility χ(t) in a mean field Potts glass from simulations in a wide range of temperatures above the theoretically predicted dynamical transition, for various system sizes up to 2560 spins. χ(t) has a maximum, with a height that diverges like (T−TD)−α, with α≈1. The timescale t ∗ associated with this maximum also approaches a singularity, and we show that its behavior is compatible with the relaxation time of the standard time-dependent spin autocorrelation function, also with respect to finite size effects. We find that χ(t) for temperatures near the transition temperature TD satisfies a dynamical scaling property.

The adaptive nature of liquidity taking in limit order books

2014

In financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A possible solution is provided by asymmetric liquidity, which states that the impact of a buy or sell order is inversely related to the probability of its occurrence. We empirically find that when the order flow predictability increases in one direction, the liquidity in the opposite side decreases, but the probability that a trade moves the price decreases significantly. While the…

Stochastic response of combined primary-secondary structures under seismic input

1992

A technique for non-stationary stochastic analysis of linear combined primary and secondary subsystems subjected to a zero-mean Gaussian base excitation is presented. The proposed technique, based on the use of the Taylor's expansion in evaluating the operators which appear in the step-by-step procedure, does not require the evaluation of the complex eigenproperties of the combined system. Operating in this way, even though the numerical procedure is a conditionally stable one, appears to be more efficient than existing methods to evaluate the dynamic response of such composite systems. It is also shown that the proposed procedure is available whether the seismic input is idealized as a fil…

THE ROLE OF UNBOUNDED TIME-SCALES IN GENERATING LONG-RANGE MEMORY IN ADDITIVE MARKOVIAN PROCESSES

2013

Any additive stationary and continuous Markovian process described by a Fokker–Planck equation can also be described in terms of a Schrödinger equation with an appropriate quantum potential. By using such analogy, it has been proved that a power-law correlated stationary Markovian process can stem from a quantum potential that (i) shows an x-2 decay for large x values and (ii) whose eigenvalue spectrum admits a null eigenvalue and a continuum part of positive eigenvalues attached to it. In this paper we show that such two features are both necessary. Specifically, we show that a potential with tails decaying like x-μ with μ &lt; 2 gives rise to a stationary Markovian process which is not p…