Sensitivity Analysis of Economic Variables using Neuro-Fuzzy Approach

Lotfi, Ehsan; Babrzadeh, S.; Khosravi, Abbas

doi:10.24200/sci.2019.5488.1305

Cited by 3 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modeling the labor market [55,56] Modeling the money market [57,58] Financial market models [59,60] Forecasting currency crises and financial risks [61,62] 10. Modeling inflation Modeling inflation [63,64] Impact of inflation on production [65,66] 11. Mathematical models of state regulation of the economy…”

Section: Mathematical Models Of Market Economymentioning

confidence: 99%

See 1 more Smart Citation

A Review of Mathematical Models of Macroeconomics, Microeconomics, and Government Regulation of the Economy

et al. 2023

View full text Add to dashboard Cite

This review analyzes articles on the mathematical modeling of economic facts and processes. Mathematical modeling of the economy has rapidly developed in the past and current centuries. This is explained by the fact that, firstly, economics does not tolerate full-scale experiments, secondly, mathematical modeling significantly improves the accuracy of research results, and, finally, thirdly, economics becomes a science only when it is based on mathematics. The article presents an overview of the main methods of economic modeling used in scientific research over the past twenty years. The review does not claim to cover all areas, methods, and models used in scientific research in the field of economics. This cannot be done in one article. Mathematical modeling of only three sections of economic theory is considered: macroeconomics, microeconomics, and state regulation of the economy. The review of research methods and models in the microeconomics section, which are available in the scientific research toolkit but have already been described in the macroeconomics section, has been omitted. Only effective, practice-tested models are used in the Review. We hope that this review will be useful to scientists involved in the indirect study of economic phenomena and processes.

show abstract

Section: Mathematical Models Of Market Economymentioning

confidence: 99%

“…To study the effect of inflation on production, the following system of equations is considered: [65,66]. Conclusion: The mathematical study of the mechanism of the emergence and selfsustaining of inflation using a three-sector model of the economy is given.…”

Section: Mathematical Models Of Market Economymentioning

confidence: 99%

A Review of Mathematical Models of Macroeconomics, Microeconomics, and Government Regulation of the Economy

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In our study, four statistical error criteria comprising mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R) are used for assessment of the goodness of a model to estimate an observed output variable. Their calculation methods are as follows [51][52][53]:…”

Section: Error Analysismentioning

confidence: 99%

One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach

2022

View full text Add to dashboard Cite

Accurate energy production forecasting is critical when planning energy for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to predict one-day-ahead energy production from the run-of-river hydroelectric power plants in Turkey was introduced in the present study. Furthermore, to compare the accuracy of prediction, the methods of adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM), ANFIS with subtractive clustering (SC), and ANFIS with grid partition (GP) were utilized. The predicted values obtained by the application of these four methods were evaluated with detected values. The correlation coefficient (R), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used as quality metrics for prediction. The comparison showed that the LSTM neural network provided higher accuracy results in short-term energy production prediction than other ANFIS models used in the study.

show abstract

Is Platinum a Real Store of Wealth?

Vochоzka

Bláhová

Rowland

2022

IJFS

View full text Add to dashboard Cite

The research goal is to determine whether platinum can be seen as a good investment. For this purpose, content analysis of documents and deep learning neural networks with recurrent neural network were used. The results show that it pays for a koruna investor (a person holding their wealth in Czech koruna) to preserve their wealth physically in the form of a precious metal—specifically, platinum. The research confirms that platinum is a store of value but also a koruna investor’s wealth multiplier. This can be due to its rare occurrence in nature, but also to its unique use in manufacturing. A research limitation is the period for which the data were used. The finding that platinum is a store of value, as well as a wealth multiplier, can thus be concretized when using the data for a five-year period. It shall also be added that no turbulent changes are anticipated (such as interruption of platinum supply, unexpected government regulation of trade, etc.).

show abstract

Sensitivity Analysis of Economic Variables using Neuro-Fuzzy Approach

Cited by 3 publications

References 27 publications

A Review of Mathematical Models of Macroeconomics, Microeconomics, and Government Regulation of the Economy

A Review of Mathematical Models of Macroeconomics, Microeconomics, and Government Regulation of the Economy

One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach

Is Platinum a Real Store of Wealth?

Contact Info

Product

Resources

About