Robust clustering of COVID-19 cases across U.S. counties using mixtures of asymmetric time series models with time varying and freely indexed covariates

2022 ◽  
pp. 1-15
Author(s):  
Mohsen Maleki ◽  
Hamid Bidram ◽  
Darren Wraith
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Time Varying ◽  
Robust Clustering

scholarly journals Forecasting tourist arrivals using time-varying parameter structural time series models

2011 ◽  
Vol 27 (3) ◽  
pp. 855-869 ◽  
Author(s):  
Haiyan Song ◽  
Gang Li ◽  
Stephen F. Witt ◽  
George Athanasopoulos
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Time Varying ◽  
Structural Time Series ◽  
Time Varying Parameter ◽  
Structural Time Series Models ◽  
Structural Time

scholarly journals Comparing Cross-Section and Time-Series Factor Models

2019 ◽  
Vol 33 (5) ◽  
pp. 1891-1926 ◽  
Author(s):  
Eugene F Fama ◽  
Kenneth R French
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Factor Models ◽  
Constant Factor ◽  
Time Series Models ◽  
Time Varying ◽  
Oxford University ◽  
Regression Approach ◽  
Fama And French ◽  
Published Paper

Abstract We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Wavelet-M-Estimation for Time-Varying Coefficient Time Series Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xingcai Zhou ◽  
Fangxia Zhu
Keyword(s):  
Time Series ◽  
Asymptotic Properties ◽  
Time Series Models ◽  
Time Varying ◽  
Bahadur Representation ◽  
Conditional Quantile ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
M Estimation ◽  
Time Varying Coefficients

This paper proposes wavelet-M-estimation for time-varying coefficient time series models by using a robust-type wavelet technique, which can adapt to local features of the time-varying coefficients and does not require the smoothness of the unknown time-varying coefficient. The wavelet-M-estimation has the desired asymptotic properties and can be used to estimate conditional quantile and to robustify the usual mean regression. Under mild assumptions, the Bahadur representation and the asymptotic normality of wavelet-M-estimation are established.

scholarly journals A flexible approach to parametric inference in nonlinear and time varying time series models

2010 ◽  
Vol 159 (1) ◽  
pp. 134-150 ◽  
Author(s):  
Gary Koop ◽  
Simon Potter
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Time Varying ◽  
Parametric Inference ◽  
Flexible Approach

Wavelet estimation in time-varying coefficient time series models with measurement errors

2017 ◽  
Vol 47 (10) ◽  
pp. 2504-2519
Author(s):  
Xing-cai Zhou ◽  
Ying-zhi Xu ◽  
Jin-guan Lin
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Time Series Models ◽  
Time Varying ◽  
Wavelet Estimation ◽  
Varying Coefficient

scholarly journals Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series Models

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5672
Author(s):  
Shabbir Ahmed ◽  
Fotis Kopsaftopoulos
Keyword(s):  
Time Series ◽  
Wave Propagation ◽  
Guided Wave ◽  
Stationary Time Series ◽  
Model Parameters ◽  
Time Series Models ◽  
Fe Model ◽  
Time Varying ◽  
Small Temperature ◽  
Guided Wave Propagation

In the context of active-sensing guided-wave-based acousto-ultrasound structural health monitoring, environmental and operational variability poses a considerable challenge in the damage diagnosis process as they may mask the presence of damage. In this work, the stochastic nature of guided wave propagation due to the small temperature variation, naturally occurring in the ambient or environment, is rigorously investigated and modeled with the help of stochastic time-varying time series models, for the first time, with a system identification point of view. More specifically, the output-only recursive maximum likelihood time-varying auto-regressive model (RML-TAR) is employed to investigate the uncertainty in guided wave propagation by analyzing the time-varying model parameters. The steps and facets of the identification procedure are presented, and the obtained model is used for modeling the uncertainty of the time-varying model parameters that capture the underlying dynamics of the guided waves. The stochasticity inherent in the modal properties of the system, such as natural frequencies and damping ratios, is also analyzed with the help of the identified RML-TAR model. It is stressed that the narrow-band high-frequency actuation for guided wave propagation excites more than one frequency in the system. The values and the time evolution of those frequencies are analyzed, and the associated uncertainties are also investigated. In addition, a high-fidelity finite element (FE) model was established and Monte Carlo simulations on that FE model were carried out to understand the effect of small temperature perturbation on guided wave signals.

Estimation of Some Bilinear Time Series Models with Time Varying Coefficients

2004 ◽  
Vol 22 (2) ◽  
pp. 355-376 ◽  
Author(s):  
Abdelouahab Bibi ◽  
Alwell J. Oyet
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Varying Coefficients

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Recent Developments
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