Robust modelling of periodic vector autoregressive time series

Ursu, Eugen; Péreau, Jean-Christophe

doi:10.1016/j.jspi.2014.07.005

Cited by 4 publications

(6 citation statements)

References 26 publications

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“…Robust estimators based on robust autocovariances for ARMA models were proposed by Bustos and Yohai (1986). Their methodology was extended for multivariate PAR models by Ursu and Pereau (2014). The system of equations obtained in Ursu and Pereau (2014, eq.…”

Section: Robust Modeling Of Par Modelsmentioning

confidence: 99%

“…where k is a constant and sgn(x) is the signum function. Generally, an iterative algorithm is proposed for ARMA models in Bustos and Yohai (1986) and for PAR models in Ursu and Pereau (2014). The convergence of this algorithm cannot be guaranteed, but the convergence always occurred in all the simulations run in Ursu and Pereau (2014).…”

Section: Robust Modeling Of Par Modelsmentioning

confidence: 99%

“…Generally, an iterative algorithm is proposed for ARMA models in Bustos and Yohai (1986) and for PAR models in Ursu and Pereau (2014). The convergence of this algorithm cannot be guaranteed, but the convergence always occurred in all the simulations run in Ursu and Pereau (2014). Other recent works on robustness in periodic time series include the estimator of PAR models proposed by Sarnaglia et al (2010).…”

Section: Robust Modeling Of Par Modelsmentioning

confidence: 99%

“…In particular the sensitivity of the LS estimation method to outliers requires the use of robust approach (Denby and Martin (1979) for autoregressive models of first order; Ben et al (1999) for vector autoregressive moving average (VARMA) models; Shao (2007) and Sarnaglia et al (2010) for univariate PAR models). In periodic vector autoregressive (PVAR) models, Ursu and Pereau (2014) implement a robust estimation method based on residual autocovariances (RA) to deal with additive outliers.…”

Section: Introductionmentioning

confidence: 99%

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Application of periodic autoregressive process to the modeling of the Garonne river flows

Ursu

Péreau

2015

Stoch Environ Res Risk Assess

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Accurate forecasting of river flows is one of the most important applications in hydrology, especially for the management of reservoir systems. To capture the seasonal variations in river flow statistics, this paper develops a robust modeling approach to identify and estimate periodic autoregressive (PAR) model in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on residual autocovariances. A genetic algorithm with Bayes information criterion is used to identify the optimal PAR model. The method is applied to average monthly and quarter-monthly flow data (1959-2010) for the Garonne river in the southwest of France. Results show that forecasts are better off in the robust model rather than the unrobust model. The accuracy of the forecasts is also improved when the model is specified in quartermonthly flows, especially for the dry seasons.

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Section: Robust Modeling Of Par Modelsmentioning

confidence: 99%

Section: Robust Modeling Of Par Modelsmentioning

confidence: 99%

Section: Robust Modeling Of Par Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of periodic autoregressive process to the modeling of the Garonne river flows

Ursu

Péreau

2015

Stoch Environ Res Risk Assess

Self Cite

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“…These models were proposed for describing time series arising in different areas such as economics, hydrology, climatology and signal processing (Hipel and McLeod 1994;Franses and Paap 2004;Ursu and Turkman 2012). Robust estimation procedures for the parameters of univariate and multivariate PAR models have been proposed in Shao (2008); Sarnaglia et al (2010); Ursu and Pereau (2014). For a review of contributions to PAR models see Franses and Paap (2004) and references therein; these models were also considered in a Bayesian framework (e. g. Vosseler and Weber 2018).…”

Section: Introductionmentioning

confidence: 99%

Detection and estimation of additive outliers in seasonal time series

2019

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The detection of outliers in a time series is an important issue because their presence may have serious negative effects on the analysis in many different ways. Moreover the presence of a complex seasonal pattern in the series could affect the properties of the usual outlier detection procedures. Therefore modelling the appropriate form of seasonality is a very important step when outliers are present in a seasonal time series. In this paper we present some procedures for detection and estimation of additive outliers when parametric seasonal models, in particular Periodic AutoRegressive, are specified to fit the data. A simulation study is presented to evaluate the benefits and the drawbacks of the proposed procedure on a selection of seasonal time series. An application to three real time series is also examined.

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Parameter Identification of Tractor‐Semitrailer Model under Steering and Braking

Shi

Qiu

2019

Mathematical Problems in Engineering

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This paper describes a valuable linear yaw-roll tractor-semitrailer (TST) model with five-degree-of-freedom (DOFs) for control algorithm development when steering and braking. The key parameters, roll stiffness, axle cornering stiffness, and fifth-wheel stiffness, are identified by the genetic algorithm (GA) and multistage genetic algorithm (MGA) based on TruckSim outputs to increase the accuracy of the model. Thus, the key parameters of the simplified model can be modified according to the real-time vehicle states by online lookup table and interpolation. The TruckSim vehicle model is built referring to the real tractor (JAC-HFC4251P1K7E33ZTF6×2) and semitrailer (Luyue LHX9406) used in the field test later. The validation of the linear yaw-roll model of a tractor-semitrailer using field test data is presented in this paper. The field test in the performance testing ground is detailed, and the test data of roll angle, roll rate, and yaw rate are compared with the outputs of the model with maps of the key parameters. The results indicate that the error of the tractor’s roll angle and semitrailer’s roll angle between model data and test data is 1.13% and 1.24%, respectively. The roll rate and yaw rate of the tractor and semitrailer are also in good agreement.

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Robust modelling of periodic vector autoregressive time series

Cited by 4 publications

References 26 publications

Application of periodic autoregressive process to the modeling of the Garonne river flows

Application of periodic autoregressive process to the modeling of the Garonne river flows

Detection and estimation of additive outliers in seasonal time series

Parameter Identification of Tractor‐Semitrailer Model under Steering and Braking

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