Determination of the Two-Dimensional ARMA Model Order Using Rank Test Based Approach

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

doi:10.1109/cca.2007.4389391

Cited by 2 publications

(1 citation statement)

References 15 publications

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Order By: Relevance

“…Following that, several order selection approaches based on information theoretic criteria, such as Akaike's information criterion (AIC) (Akaike 1974), Akaike's final prediction error (FPE) (Akaike 1970), minimum description length (MDL) (Rissanen 1978;Liang et al 1993) and so on, have been developed. 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.…”

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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