A Gaussian process regression machine learning model for forecasting retail property prices with Bayesian optimizations and cross-validation

Xu, Xiaojie; Zhang, Yun

doi:10.1016/j.dajour.2023.100267

Cited by 29 publications

(2 citation statements)

References 158 publications

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“…GPR is categorized as a kernel-based approach in machine learning since it may employ a number of kernels depending on the data being studied [22]. For the retrieval of biophysical parameters in remote sensing applications, several kernel-based strategies have been investigated in the literature, including support vector machine (SVM), relevance vector machine (RVM), and GPR.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Numerical Analysis of Gas Hold-Up of Two-Phase Ebullated Bed Reactor

Almukhtar,

Yahya,

Mahdy

et al. 2023

ChemEngineering

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Due to the significant increase in heavy feedstocks being transported to refineries and the hydrocracking process, the significance of adopting an ebullated bed reactor has been reemphasized in recent years. The predictive modelling of gas hold-up in an ebullated two-phase reactor was performed using 10 machine learning methods based on support vector machine (SVM) and Gaussian process regression (GPR) in this study. In an ebullated bed reactor, the impacts of three features, namely liquid velocity, gas velocity, and recycling ratio, on the gas hold-up were examined. The liquid velocity has the most impact on the predicted gas hold-up, according to the feature significance analysis. The rotational-quadratic, squared-exponential, Matern 5/2, and exponential kernel functions integrated with the GPR models and the linear, quadratic, cubic, fine, medium, and coarse kernel functions integrated with the SVM model performed well during training and testing, with the exception of the fine SVM model, whose R2 is very low. According to the R2 > 0.9 and low RMSE and MAE values, the rotational-quadratic, squared-exponential, and Matern 5/2 GPR models performed the best.

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Section: Gaussian Process Regressionmentioning

confidence: 99%

Numerical Analysis of Gas Hold-Up of Two-Phase Ebullated Bed Reactor

Almukhtar,

Yahya,

Mahdy

et al. 2023

ChemEngineering

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“…Some fundamental time series econometric models, such as the autoregressive (AR), vector autoregressive (VAR), vector error correction (VEC) and a wide spectrum of their variations and extensions, have been applied for various forecasting purposes and uses (Kim et al ., 2007; Xu and Zhang, 2021c, 2022c, 2023e; Xu, 2015, 2017a, b, c, 2018b, c, e, 2019a, b, c, 2020; Zohrabyan et al ., 2008; Cabrera et al ., 2011; Kouwenberg and Zwinkels, 2014; Webb et al ., 2016; Yang et al ., 2018; Milunovich, 2020). Recently, a wide variety of machine learning methods and algorithms, such as the random forest, regression tree, support vector regression, nearest neighbor, neural network, bagging, boosting, ensemble learning and deep learning, have been found to be useful and promising tools to various forecasting problems (Xu and Zhang, 2023) regarding house price time series data (Wang et al ., 2014; Xu and Zhang, 2023i; Park and Bae, 2015; Plakandaras et al ., 2015; Chen et al ., 2017; Liu and Liu, 2019; Huang, 2019; Li et al ., 2020; Yan and Zong, 2020; Milunovich, 2020; Pai and Wang, 2020; Ho et al ., 2021; Rico-Juan and de La Paz, 2021; Xu and Li, 2021; Embaye et al ., 2021). Particularly, neural networks have been seen in the literature to have great potential to forecast (economic/financial) time series that tend to be highly noised and chaotic (Xu and Zhang, 2021d, 2022l, n, 2023c, d, h, l; Wang and Yang, 2010; Yang et al ., 2008, 2010; Wegener et al ., 2016), including different types of house price time series (Xu and Zhang, 2021b, 2022g, i, j, k, 2023q; Wilson et al ., 2002; Taffese, 2007; Selim, 2009; Wang et al ., 2016; Abidoye and Chan, 2017; Li et al ., 2017;…”

Section: Introductionmentioning

confidence: 99%

Composite property price index forecasting with neural networks

Xu,

Zhang

2023

Self Cite

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PurposeThe Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.Design/methodology/approachThe goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.FindingsThe authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.Originality/valueResults here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

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Forecasting wholesale prices of yellow corn through the Gaussian process regression

Jin,

2024

Neural Comput & Applic

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A Gaussian process regression machine learning model for forecasting retail property prices with Bayesian optimizations and cross-validation

Cited by 29 publications

References 158 publications

Numerical Analysis of Gas Hold-Up of Two-Phase Ebullated Bed Reactor

Numerical Analysis of Gas Hold-Up of Two-Phase Ebullated Bed Reactor

Composite property price index forecasting with neural networks

Forecasting wholesale prices of yellow corn through the Gaussian process regression

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