Estimating the Romanian Economic Sentiment Indicator Using Artificial Intelligence Techniques

Ilie, Constantin; Ilie, Margareta; Melnic, Lucia Violeta; Topalu, Ana-Maria

doi:10.5171/2011.966864

Cited by 3 publications

(2 citation statements)

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“…Then the estimation error is measured in any case and used to estimate the network quality. This step determines the success of education by simply comparing the statistical data with the data predicted by ANN [17]. The correlation coefficient (R = 0,996) between the actual data and the predicted (modeled) data, while the Determination coefficient (R 2 = 0,992) and the errors are small.…”

Section: Testing and Verificationmentioning

confidence: 99%

Eksik sıcaklık verilerinin Yapay Sinir Ağları (YSA) ile tahmin edilmesi

Katipoğlu¹,

Acar²

2021

DÜMF Mühendislik Dergisi

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Ensuring more reliable and quality meteorological and climatological studies by providing data continuity and widening the data range. For this reason, missing values in meteorological data such as temperature, precipitation, evaporation must be completed. In this study, an artificial neural network (ANN) model was used to complete missing temperature data in the Horasan meteorology station. To establish the ANN model, monthly average temperature values of neighboring stations having similar climatic characteristics and altitude with Horasan were used as input. The monthly average temperature values of the Horasan station were used as output. Approximately 70% of the data was used for training, about 15% for testing, and about 15% for verification in the ANN model. Various statistical parameters were compared to determine the best network architecture and best model. As a result, the model's high determination coefficient (R2 = 0.99) and low mean absolute error (MAE = 0.61) showed that the ANN model can be used effectively in estimating missing temperature data.

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Section: Testing and Verificationmentioning

confidence: 99%

Eksik sıcaklık verilerinin Yapay Sinir Ağları (YSA) ile tahmin edilmesi

Katipoğlu¹,

Acar²

2021

DÜMF Mühendislik Dergisi

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“…Research of this type was also done by Ilie et al (2017, p. 403 and 2012, p. 5) [15][16][17] and represents new attempts to use artificial intelligence for the determination of influences and forecasting the economic and managerial processes and systems, in order to be able to prepare for the probable future convulsion of the financial, economic or social sectors and for better decision making.…”

Section: Introductionmentioning

confidence: 99%

Sustainability through the Use of Modern Simulation Methods—Applied Artificial Intelligence

et al. 2019

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As the transformative power of AI crosses all economic and social sectors, the use of it as a modern technique for the simulation and/or forecast of various indicators must be viewed as a tool for sustainable development. The present paper reveals the results of research on modeling and simulating the influences of four economic indicators (the production in industry, the intramural research and development expenditure, the turnover and volume of sales and employment) on the evolution of European Economic Sentiment using artificial intelligence. The main goal of the research was to build, train and validate an artificial neural network that is able to forecast the following year’s value of economic sentiment using the present values of the other indicators. Research on predicting European Economic Sentiment Indicator (ESI) using artificial neural networks is a starting point, with work on this subject almost inexistent, the reason being mainly that ESI is a composite of five sectoral confidence indicators and is not thought to be an emotional response to the interaction of the entrepreneurial population with different economic indicators. The authors investigated, without involving a direct mathematical interaction among the indicators involved, predicting ESI based on a cognitive response. Considering the aim of the research, the method used was simulation with an artificial neural network and a feedforward network (structure 4-9-6-1) and a backward propagation instruction algorithm was built. The data used are euro area values (for 19 countries only—EA19) recorded between 1999 and 2016, with Eurostat as the European Commission’s statistical data website. To validate the results, the authors imposed the following targets: the result of the neural network training error is less than 5% and the prediction verification error is less than 10%. The research outcomes resulted in a training error (after 30,878 iterations) of less than 0.099% and a predictive check error of 2.02%, which resulted in the conclusion of accurate training and an efficient prediction. AI and artificial neural networks, are modeling and simulation methods that can yield results of nonlinear problems that cover, for example, human decisions based on human cognitive processes as a result of previous experiences. ANN copies the structure and functioning of the biological brain, having the advantage through learning and coaching processes (biological cognitive), to copy/predict the results of the thinking process and, thus, the process of choice by the biological brain. The importance of the present paper and its results stems from the authors’ desire to use and popularize modern methods of predicting the different macroeconomic indices that influence the behavior of entrepreneurs and therefore the decisions of these entrepreneurs based on cognitive response more than considering linear mathematical functions that cannot correctly understand and anticipate financial crises or economic convulsions. Using methods such as AI, we can anticipate micro- and macroeconomic developments, and therefore react in the direction of diminishing their negative effects for companies as well as the national economy or European economy.

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Monthly Stream Flows Estimation in the Karasu River of Euphrates Basin With Artificial Neural Networks Approach

Katipoğlu

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

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This study aims to estimate streamflow values with artificial neural networks (ANN) using various meteorological parameters. In developing the ANN model, various combinations of precipitation, air temperatures, and potential evapotranspiration values were used as inputs, and streamflow values were obtained. Meteorological data is divided into 70% train, 15% test, and 15% validation. In the model's design, various training algorithms, network architecture, input combinations, and the number of iterations were tried, and the most suitable model was tested. Correlation coefficient (R), coefficient of determination (R2), absolute error (AE), and absolute relative error (ARE) coefficients were compared, and the most suitable model was selected. According to the analysis results, the optimal model was obtained using 2000 iterations, the architecture of the 4-4-1 model, and the Quasi-Newton algorithm. It was determined that the ANNs successfully modeled the rainfall-runoff relationship and produced reliable estimates. In addition, it was revealed that the inclusion of potential evapotranspiration values obtained by the Thornthwaite method into the model increases the model's success.

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Estimating the Romanian Economic Sentiment Indicator Using Artificial Intelligence Techniques

Cited by 3 publications

References 0 publications

Eksik sıcaklık verilerinin Yapay Sinir Ağları (YSA) ile tahmin edilmesi

Eksik sıcaklık verilerinin Yapay Sinir Ağları (YSA) ile tahmin edilmesi

Sustainability through the Use of Modern Simulation Methods—Applied Artificial Intelligence

Monthly Stream Flows Estimation in the Karasu River of Euphrates Basin With Artificial Neural Networks Approach

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