Cross validation for uncertain autoregressive model

Liu, Zhe; Yang, Xiangfeng

doi:10.1080/03610918.2020.1747077

Cited by 37 publications

(14 citation statements)

References 18 publications

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“…Ultimately, the model is only evaluated after it has been trained. Along with model training, cross-validation aims to find an optimal model with the best performance [39]. In this work, the training set was built using 70% of the records collected, and the test set was built using the remaining 30% of the whole dataset.…”

Section: Resultsmentioning

confidence: 99%

Numerical Simulation for the Sound Absorption Properties of Ceramic Resonators

Ciaburro

Iannace

2020

Fibers

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This work reports the results of experimental measurements of the sound absorption coefficient of ceramic materials using the principle of acoustic resonators. Subsequently, the values obtained from the measurements were used to train a simulation model of the acoustic behavior of the analyzed material based on artificial neural networks. The possible applications of sound-absorbing materials made with ceramic can derive from aesthetic or architectural needs or from functional needs, as ceramic is a fireproof material resistant to high temperatures. The results returned by the simulation model based on the artificial neural networks algorithm are particularly significant. This result suggests the adoption of this technology to find the finest possible configuration that allows the best sound absorption performance of the material.

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Section: Resultsmentioning

confidence: 99%

Numerical Simulation for the Sound Absorption Properties of Ceramic Resonators

Ciaburro

Iannace

2020

Fibers

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“…In order to select the order k in uncertain ARMA( k , 0) model, we employ the method of fold rolling origin cross validation proposed by Liu and Yang ( 2020a ) based on the data . Consider the maximum order 20 and .…”

Section: Uncertain Time Series Analysis For Covid-19 Casesmentioning

confidence: 99%

“…Yang and Ni ( 2020 ) introduced another uncertain time series model, the uncertain moving average model, and converted the 1-order uncertain moving average model into an uncertain autoregressive model. Liu and Yang ( 2020a ) offered a cross-validation process to determine the order of the uncertain autoregressive model. Tang ( 2020 ) extended the uncertain autoregressive model in the sense of vector situation and developed an uncertain vector autoregressive model.…”

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of confirmed COVID-19 cases in China with uncertain time series

Yang

2020

Fuzzy Optim Decis Making

Self Cite

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This paper presents an uncertain time series model to analyse and predict the evolution of confirmed COVID-19 cases in China, excluding imported cases. Compared with the results of the classical time series model, the uncertain time series model could better describe the COVID-19 epidemic by using an uncertain hypothesis test to filter out outliers. This improvement is reflected in the two observations. One is that the estimated variance of the disturbance term in the uncertain time series model is more appropriate and acceptable than that in the classical time series model, and the other is that the disturbance term of the classical time series model cannot be regarded as a random variable but as an uncertain variable. Keywords Uncertainty theory • Uncertain time series • Uncertain hypothesis test • COVID-19

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“…For example, Yang and Liu (2019) first presented uncertain time series analysis to predict future values based on imprecise observations. Following that, more research interests (Yang and Ni 2020;Liu and Yang 2020b;Tang 2020) were drawn to study and extend this topic. Especially, Ye and Yang (2020) applied uncertain time series to modelling cumulative numbers of COVID-19 infections.…”

Section: Introductionmentioning

confidence: 99%

Uncertain growth model for the cumulative number of COVID-19 infections in China

Liu

2020

Fuzzy Optim Decis Making

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As a type of coronavirus, COVID-19 has quickly spread around the majority of countries worldwide, and seriously threatens human health and security. This paper aims to depict cumulative numbers of COVID-19 infections in China using the growth model chosen by cross validation. The residual plot does not look like a null plot, so we can not find a distribution function for the disturbance term that is close enough to the true frequency. Therefore, the disturbance term can not be characterized as random variables, and stochastic regression analysis is invalid in this case. To better describe this pandemic automatically, this paper first employs uncertain growth models with the help of uncertain hypothesis tests to detect and modify outliers in data. The forecast value and confidence interval for the cumulative number of COVID-19 infections in China are provided.

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Cross validation for uncertain autoregressive model

Cited by 37 publications

References 18 publications

Numerical Simulation for the Sound Absorption Properties of Ceramic Resonators

Numerical Simulation for the Sound Absorption Properties of Ceramic Resonators

Analysis and prediction of confirmed COVID-19 cases in China with uncertain time series

Uncertain growth model for the cumulative number of COVID-19 infections in China

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