Heat transfer correlations by symbolic regression

Cai, Weihua; Pacheco-Vega, Arturo; Sen, Mihir; Yang, K. T.

doi:10.1016/j.ijheatmasstransfer.2006.04.029

Cited by 57 publications

(26 citation statements)

References 19 publications

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“…The wide applicability of this approach is attracting researchers from different areas (Sarradj and Geyer, 2014;Ceperic, Bako and Baric, 2014;Barmpalexis et al, 2011;Wu, Chou and Su, 2008;Cai et al, 2006;). SR plays an increasingly important role in many engineering applications, such as signal processing (Yao and Lin, 2009), industrial data analysis , and experimental design of manufacturing systems (Can and Heavey, 2011).…”

Section: Symbolic Regressionmentioning

confidence: 99%

See 1 more Smart Citation

Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

González

Monte

Porras

2016

Eastern European Economics

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Abstract. Tendency surveys are the main source of agents' expectations. The main aim of this study is twofold. First, we propose a new method to quantify survey-based expectations by means of symbolic regression (SR) via genetic programming. Second, we combine the main SR-generated indicators to estimate the evolution of GDP, obtaining the best results in the Czech Republic and Hungary. Finally, we assess the impact of the 2008 financial crisis, finding an improvement in the capacity of agents' expectations in most Central and Eastern European economies to anticipate economic growth after the crisis.

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Section: Symbolic Regressionmentioning

confidence: 99%

“…Empirical modelling via SR with GP is increasingly attracting interest from different areas due to its wide applicability (Cai et al, 2006;Yao andLin, 2009, Vladislavleva et al, 2010;Can and Heavey, 2011;Barmpalexis et al, 2011;Ceperic et al, 2014;Sarradj and Geyer, 2014). Nevertheless, the applications in economics are still few.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

González

Monte

Porras

2016

Eastern European Economics

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“…Due to its versatility, SR is being increasingly used in different areas: from industrial data analysis and the experimental design of manufacturing systems (Can and Heavey, 2011), to signal processing (Yao and Lin, 2009) and other various applications (Barmpalexis et al, 2011;Cai et al, 2006;Ceperic et al, 2014;Sarradj and Geyer, 2014;Wu et al, 2008).…”

Section: Evolutionary Computationmentioning

confidence: 99%

A new approach for the quantification of qualitative measures of economic expectations

2016

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In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance. We obtain the best results for Belgium, Norway, Austria, Lithuania, Japan and the United Kingdom.

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“…Given its versatility, SR has been increasingly used in different areas (Barmpalexis et al, 2011;Cai et al, 2006;Can and Heavey, 2011;Ceperic et al, 2014;Sarradj and Geyer, 2014;Vladislavleva et al, 2010;Wu et al, 2008;Yang et al, 2015;Yao and Lin, 2009;Zameer et al, 2017), but there have been very few SR applications in macroeconomics. Claveria et al (2016) implemented SR via GP to derive a set of building blocks used to estimate economic activity.…”

Section: Literature Reviewmentioning

confidence: 99%

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

2017

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Heat transfer correlations by symbolic regression

Cited by 57 publications

References 19 publications

Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

A new approach for the quantification of qualitative measures of economic expectations

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

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