Search results for "Autoregressive model"
showing 10 items of 120 documents
Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe
2021
Abstract Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, whi…
Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders
2020
This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the sugg…
A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data
2021
Growing interest in understanding microbiota dynamics has motivated the development of different strategies to model microbiota time series data. However, all of them must tackle the fact that the available data are high-dimensional, posing strong statistical and computational challenges. In order to address this challenge, we propose a Dirichlet autoregressive model with time-varying parameters, which can be directly adapted to explain the effect of groups of taxa, thus reducing the number of parameters estimated by maximum likelihood. A strategy has been implemented which speeds up this estimation. The usefulness of the proposed model is illustrated by application to a case study.
Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals
2004
Although the concept of transfer function is intrinsically related to an input-output relationship, the traditional and widely used estimation method merges both feedback and feedforward interactions between the two analyzed signals. This limitation may endanger the reliability of transfer function analysis in biological systems characterized by closed loop interactions. In this study, a method for estimating the transfer function between closed loop interacting signals was proposed and validated in the field of cardiovascular and cardiorespiratory variability. The two analyzed signals x and y were described by a bivariate autoregressive model, and the causal transfer function from x to y w…
Categorizing the Role of Respiration in Cardiovascular and Cerebrovascular Variability Interactions
2022
Objective: Respiration disturbs cardiovascular and cerebrovascular controls but its role is not fully elucidated. Methods: Respiration can be classified as a confounder if its observation reduces the strength of the causal relationship from source to target. Respiration is a suppressor if the opposite situation holds. We prove that a confounding/suppression (C/S) test can be accomplished by evaluating the sign of net redundancy/synergy balance in the predictability framework based on multivariate autoregressive modelling. In addition, we suggest that, under the hypothesis of Gaussian processes, the C/S test can be given in the transfer entropy decomposition framework as well. Experimental p…
Testing Frequency-Domain Causality in Multivariate Time Series
2010
We introduce a new hypothesis-testing framework, based on surrogate data generation, to assess in the frequency domain, the concept of causality among multivariate (MV) time series. The approach extends the traditional Fourier transform (FT) method for generating surrogate data in a MV process and adapts it to the specific issue of causality. It generates causal FT (CFT) surrogates with FT modulus taken from the original series, and FT phase taken from a set of series with causal interactions set to zero over the direction of interest and preserved over all other directions. Two different zero-setting procedures, acting on the parameters of a MV autoregressive (MVAR) model fitted on the ori…
Time-Varying Surrogate Data to Assess Nonlinearity in Nonstationary Time Series: Application to Heart Rate Variability
2009
We propose a method to extend to time-varying (TV) systems the procedure for generating typical surrogate time series, in order to test the presence of nonlinear dynamics in potentially nonstationary signals. The method is based on fitting a TV autoregressive (AR) model to the original series and then regressing the model coefficients with random replacements of the model residuals to generate TV AR surrogate series. The proposed surrogate series were used in combination with a TV sample entropy (SE) discriminating statistic to assess nonlinearity in both simulated and experimental time series, in comparison with traditional time-invariant (TIV) surrogates combined with the TIV SE discrimin…
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
2021
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…
Improving spatial temperature estimates by resort to time autoregressive processes
2012
Temperature estimation methods usually involve regression followed by kriging of residuals (residual kriging). Despite the performance of such models, there is invariably a residual which is not necessarily unpredictable because it may still be correlated in time. We set out to analyse such residuals through resort to autoregressive processes. It is shown that the optimal period varies depending on whether it is identified by functions of the form resd = f(resd−1, resd−2, ..., resd−p) or by partial correlations. Autoregressive processes significantly improve estimates, which are evaluated by cross-validations. Finally, the two following points are discussed: (1) the assumptions of the autor…
Stochastic differential calculus for wind-exposed structures with autoregressive continuous (ARC) filters
2008
In this paper, an alternative method to represent Gaussian stationary processes describing wind velocity fluctuations is introduced. The technique may be considered the extension to a time continuous description of the well-known discrete-time autoregressive model to generate Gaussian processes. Digital simulation of Gaussian random processes with assigned auto-correlation function is provided by means of a stochastic differential equation with time delayed terms forced by Gaussian white noise. Solution of the differential equation is a specific sample of the target Gaussian wind process, and in this paper it describes a digitally obtained record of the wind turbolence. The representation o…