Two-dimensional ARMA model order determination

Sadabadi, M. S.; Shafiee, Masoud; Karrari, M.

doi:10.1016/j.isatra.2009.04.001

Cited by 23 publications

(5 citation statements)

References 33 publications

(36 reference statements)

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“…1 (a)-(b) and (c)-(d) respectively). Besides, in the last years, several theoretical properties have been studied, and multiple applications have been developed for these processes ( [6,41,11,34,46,45,35]).…”

Section: The Spatial Arma Modelsmentioning

confidence: 99%

Robust estimation for spatial autoregressive processes based on bounded innovation propagation representations

Britos

Ojeda

2018

Comput Stat

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Robust methods have been a successful approach to deal with contaminations and noises in image processing. In this paper, we introduce a new robust method for two-dimensional autoregressive models. Our method, called BMM-2D, relies on representing a two-dimensional autoregressive process with an auxiliary model to attenuate the effect of contamination (outliers). We compare the performance of our method with existing robust estimators and the least squares estimator via a comprehensive Monte Carlo simulation study which considers different levels of replacement contamination and window sizes. The results show that the new estimator is superior to the other estimators, both in accuracy and precision. An application to image filtering highlights the findings and illustrates how the estimator works in practical applications.

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Section: The Spatial Arma Modelsmentioning

confidence: 99%

Robust estimation for spatial autoregressive processes based on bounded innovation propagation representations

Britos

Ojeda

2018

Comput Stat

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“…There are many ways to determine the orders [31], such as Loss Function, Akaike Final Prediction Error and Akaike Information Criterion (AIC). We adopt the AIC to select the orders of the ARX models.…”

Section: Arx Model-set Of the Quadrotormentioning

confidence: 99%

Modeling and control approach to a distinctive quadrotor helicopter

Peng

Chen

et al. 2014

ISA Transactions

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“…The another common method, namely, the linear algebraic method based upon the determinant and rank testing algorithms, was proposed in (Fuchs 1987;Sadabadi et al 2007). Apart from two methods above, many other methods like bayesian information criterion (BIC) (Schwarz 1978), edge detection-based approach (Al-Smadi & Al-Zaben 2005), optimal instrumental variable (IV) algorithm (Sadabadi et al 2009) and so on were investigated to estimate the order of the time series model. Another estimation problem have also considerably investigated in the aspect of coefficients determination of time series model.…”

Section: Introductionmentioning

confidence: 99%

Uncertain time series forecasting method for the water demand prediction in Beijing

Wang

Guo

2021

Water Supply

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Water demand prediction is crucial for effectively planning and management of water supply systems to handle the problem of water scarcity. Taking into account the uncertainties and imprecisions within the framework of water demand forecasting, the uncertain time series prediction method is introduced for the water demand prediction. Uncertain time series is a sequence of imprecisely observed values that are characterized by uncertain variables and the corresponding uncertain autoregressive model is employed to describe it for predicting the future values. The main contributions of this paper are shown as follows. Firstly, by defining the auto-similarity of uncertain time series, the identification algorithm of uncertain autoregressive model order is proposed. Secondly, a new parameter estimation method based on the uncertain programming is developed. Thirdly, the imprecisely observed values are assumed as the linear uncertain variables and a ratio-based method is presented for constructing the uncertain time series. Finally, the proposed methodologies are applied to model and forecast the Beijing's water demand under different confidence levels and compared with the traditional time series, i.e., ARIMA method. The experimental results are evaluated on the basis of performance criteria, which shows that the proposed method outperforms over the ARIMA method for water demand prediction.

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Two-dimensional ARMA model order determination

Cited by 23 publications

References 33 publications

Robust estimation for spatial autoregressive processes based on bounded innovation propagation representations

Robust estimation for spatial autoregressive processes based on bounded innovation propagation representations

Modeling and control approach to a distinctive quadrotor helicopter

Uncertain time series forecasting method for the water demand prediction in Beijing

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