A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX

Wei, Liang-Ying

doi:10.1016/j.econmod.2013.06.009

Cited by 64 publications

(34 citation statements)

References 20 publications

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“…Vasilakis et al (2013) presented a GP-based technique to predict returns in the trading of the euro/dollar exchange rate. Wei (2013) used an adaptive expectation GA to optimize a fuzzy model to forecast stock price trends in Taiwan. For a review of the applications of GAs for financial forecasting see Drake and Marks (2002).…”

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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Section: Evolutionary Computationmentioning

confidence: 99%

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

2016

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“…Kronberger et al (2011) use SR to identify variable interactions in a large dataset of economic indicators to estimate US inflation. Kľúčik (2012) uses GP in the estimation of total exports and imports to Slovakia via SR. Wei (2013) proposes a hybrid model to forecast the stock index in Taiwan. Yang et al (2015) propose a data-driven approach that uses SR to forecast oil production.…”

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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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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“…The selection probability of an individual is distributed on a roulette. The greater the fitness, the larger the probability of being selected as a parent individual [20] [21].…”

Section: Course Scheduling Through Genetic Algorithmmentioning

confidence: 99%

Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

Wang¹

2018

Int. J. Emerg. Technol. Learn.

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Abstract-Traditional artificial intelligence and computer-aided course scheduling schemes can no longer meet the increasing demands caused by the informatization of teaching management in colleges and universities. To address this problem, this study designed an improved adaptive genetic algorithm that is based on hard and soft constraints for course scheduling. First, the mathematical model of the genetic algorithm was established. The combination of time, teacher, and course number was regarded as the gene coding. The weekly course schedule of each class was a chromosome, and the course schedule of the entire school was the initial population. The fitness was designed according to the priority of each class, curriculum dispersion, and teacher satisfaction. Local columns between individuals were selected through the roulette principle for a variation of crossover and random columns. Iterative calculation was implemented on the basis of the default mutation and crossover rates to study the optimal course scheduling scheme. Experimental results demonstrate that the improved adaptive genetic algorithm is superior to the original genetic algorithm. When the number of iterations is 150, population evolution is optimal and the fitness does not increase. When the population size is 150 classes, the average scheduling time is the shortest. The basic, adaptive, and improved adaptive genetic algorithms are compared in terms of the number of average iterations required for convergence, maximum individual fitness, and average individual fitness. Comparison results show that the improved adaptive genetic algorithm is superior to the two other algorithms. This study provides references for the model building and evaluation of course scheduling in colleges and universities.

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A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX

Cited by 64 publications

References 20 publications

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

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

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

Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

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