The Phillips curve in Iran: econometric versus artificial neural networks

Jalaee, Sayyed Abdolmajid; Lashkary, Mehrdad; GhasemiNejad, Amin

doi:10.1016/j.heliyon.2019.e02344

Cited by 15 publications

(11 citation statements)

References 21 publications

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“…Different measures are applied to examine the estimation accuracy and forecasting ability of different estimators. e most popular ones are mean squared error (MSE) or root mean squared error (RMSE) [13] and correlation coefficient (R).…”

Section: Optimization and Methods Validationmentioning

confidence: 99%

“…e number of layers is dependent on the problem complexity. In general, a neural network is a set of connected input and output units that each connection has an associated weight [13].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…According to what has been presented, using nonclassic methods for the identification and prediction of complex systems-related problems has been expanded [13]. Hence, in this study, a novel approach for oil consumption forecasting is presented.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

Bahmani

Nejati

GhasemiNejad

et al. 2021

Mathematical Problems in Engineering

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In this paper, we develop a function of population, GDP, import, and export by applying a hybrid bat algorithm (BAT) and artificial neural network (ANN). We apply these methods to forecast oil consumption in Iran. For this purpose, an improved artificial neural network (ANN) structure, which is optimized by the BAT is proposed. The variables between 1980 and 2017 were used, partly for installing and testing the method. This method would be helpful in forecasting oil consumption and would provide a level playing field for checking the energy policy authority impacts on the structure of the energy sector in an economy such as Iran with high economic interventionism by the government. The result of the model shows that the findings are in close agreement with the observed data, and the performance of the method was excellent. We demonstrate that its efficiency could be a helpful and reliable tool for monitoring oil consumption.

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Section: Optimization and Methods Validationmentioning

confidence: 99%

“…e number of layers is dependent on the problem complexity. In general, a neural network is a set of connected input and output units that each connection has an associated weight [13].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

See 1 more Smart Citation

A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

Bahmani

Nejati

GhasemiNejad

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each neuron connection in different layers has its weights and the network learns the pattern in input and output variables by adjusting these weights during the training phase. According to the learning methods, neural networks can generally be classified into two categories: supervised learning and unsupervised learning [48]. In supervised learning, the correct response (output) for each input pattern is given to the network.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

et al. 2021

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Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

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“…The use of non-classical methods to identify and predict complex systems problems has been expanded [5] . There are many methods to predict natural phenomena around the world, but it is still difficult to accurately forecast the events.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid method based on Cuckoo optimization algorithm and artificial neural network to forecast world's carbon dioxide emission

et al. 2021

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This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo Optimization Algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO 2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases. The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control. The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies.

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The Phillips curve in Iran: econometric versus artificial neural networks

Cited by 15 publications

References 21 publications

A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

A novel hybrid method based on Cuckoo optimization algorithm and artificial neural network to forecast world's carbon dioxide emission

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