Gross domestic product estimation based on electricity utilization by artificial neural network

Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar

doi:10.1016/j.physa.2017.07.023

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…In a similar study, the gross domestic product growth rate of the European Union countries are modelled dependent on the combination of trade, imports and exports using extreme machine learning and the artificial neural networks with back propagation algorithms and it is shown that the extreme machine learning method provides better accuracy than the artificial neural networks with the back propagation algorithms [57]. In another work, the extreme machine learning model and the artificial neural network with back propagation algorithm are utilized to model the gross domestic product of the European Union countries dependent on the electricity usage where it is concluded that the extreme learning algorithm provides better modelling compared to the artificial neural network with back propagation method [58]. The gross domestic product of the United States is forecasted employing artificial neural networks in another study where it is shown that the artificial neural network model has higher accuracy compared to the dynamic factor model [59].…”

Section: Modelling the Gross Domestic Product And The Per Capita Inco...mentioning

confidence: 99%

Modelling the Gross Domestic Product and the Per Capita Income of Türkiye using Autoregressive Deep Learning Networks

Tuncsiper¹

2023

IJSSHR

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The gross domestic product and the per capita income are leading indicators regarding the income and the wealth of nations. The gross domestic product and the per capita income can be modelled dependent on various econometric data such as the export revenue, tourism revenue, trade deficit and the industrial revenue. In this work, an alternative and novel method is presented for the modelling of the gross domestic product and the per capita income. In this study, the gross domestic product and the per capita income are modelled autoregressively employing deep learning networks namely autoregressive deep learning networks. The input data of the developed deep learning networks are taken as the past values of the modelled variable making the deep learning networks effectively autoregressive models. As application examples of the autoregressive deep learning models, the gross domestic product and the per capita income data of Türkiye for the period of 1960-2021 are separately modelled. The autoregressive deep learning networks are developed in Python programming language. The coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the developed models are also computed. The plots of the results of the developed autoregressive deep learning models and the performance metrics of the models show that the developed autoregressive deep learning models can be utilized to accurately model the gross domestic product and the per capita income.

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Section: Modelling the Gross Domestic Product And The Per Capita Inco...mentioning

confidence: 99%

Modelling the Gross Domestic Product and the Per Capita Income of Türkiye using Autoregressive Deep Learning Networks

Tuncsiper¹

2023

IJSSHR

View full text Add to dashboard Cite

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“…Application of ANN to macroeconomic variable forecasting has been demonstrated, for example, in a study by Galeshchuk [61], that predicted the exchange rates and that forecasted the Indian monthly inflation rate [36]. While Stevanović et al [62], and Xu et al [11] created a model called Artificial Neural Network Mixed Data Sampling (ANN-MIDAS) in recent years to investigate the nonlinear pattern contained in the variables, and the use of China's monthly inflation rate estimate serves as evidence of its efficacy.…”

Section: Artificial Neural Network (Ann) and Midasmentioning

confidence: 99%

“…This work suggests an improved QRNN (iQRNN) that leverages well-known deep-learning techniques. It is superior to regular QRNNs in terms of accuracy, stability, and computing efficiency [62].…”

Section: Artificial Neural Network (Ann) and Midasmentioning

confidence: 99%

Improving forecasting accuracy using quantile regression neural network combined with unrestricted mixed data sampling

Hassan,

Ismail

2023

J. Nig. Soc. Phys. Sci.

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A traditional regression method involving time series variables is often observed at the same frequencies. In a situation where the frequencies differ, the higher ones are averaged or aggregated to the lower frequency. A Mixed Data Sampling (MIDAS) regression model was introduced to address such problems. In any country, stakeholders are interested in monitoring and forecasting accurately the Gross Domestic Product (GDP) using the dynamics of macroeconomic variables. We applied the hybrid QRNN-U-MIDAS model to forecast quarterly GDP using monthly and weekly data. The Quantile Regression Neural Network (QRNN) is designed to model nonlinear relationships amongst data sampled at the same frequency. Therefore, we take advantage of QRNN skills using the optimization techniques of gradient descent-based algorithms to optimise the estimated loss function Ea (\tau), and introduce them into the U-MIDAS framework, which can handle mixed data frequencies, and construct a QRNN-U-MIDAS model. The suggested hybrid QRNN-U-MIDAS model was implemented in an R-package that we created to perform both simulation and real-time data applications. The findings indicate that the QRNN-U-MIDAS regression model outperforms competing models in terms of its capacity for prediction across the conditional distribution of a response variable with a comprehensive view of the information contained in the variables, which is lacking in other competing models like U-MIDAS, ANN-U-MIDAS etc. More so, this novel model will add to the existing works of literature on robust forecasting models.

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“…Llzetzki (2018) stated that this would mean an increase in GDP, which does not translate to job creation, as it may mean that South Africa is using international companies instead of local companies to address power challenges. In other words, this could mean that South African power utilities are incurring expenses in foreign currencies (Stevanović, Vujičić and Gajić, 2018). The literature from various sources presented indicators that signifies that the power utility is likely to have financial bankruptcy such as the following:…”

Section: Compared To South Africamentioning

confidence: 99%

Application of Systems Thinking in Reviewing Power- Infrastructure Capital Investment in South Africa

Chili

Zondi²

2022

SSRN Journal

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Gross domestic product estimation based on electricity utilization by artificial neural network

Cited by 14 publications

References 13 publications

Modelling the Gross Domestic Product and the Per Capita Income of Türkiye using Autoregressive Deep Learning Networks

Modelling the Gross Domestic Product and the Per Capita Income of Türkiye using Autoregressive Deep Learning Networks

Improving forecasting accuracy using quantile regression neural network combined with unrestricted mixed data sampling

Application of Systems Thinking in Reviewing Power- Infrastructure Capital Investment in South Africa

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