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Mixed modified fruit fly optimization algorithm with general regression neural network to build oil and gold prices forecasting model
When facing a clouded global economy, many countries would increase their gold reserves.
On the other hand, oil supply and demand depends on the political and economic situations of oil
producing countries and their production technologies. Both oil and gold reserve play important roles
in the economic development of a country. The paper aims to discuss this issue.
Design/methodology/approach – This paper uses the historical data of oil and gold prices
as research data, and uses the historical price tendency charts of oil and gold, as well as cluster
analysis, to discuss the correlation between the historical data of oil and gold prices. By referring
to the technical index equation of stocks, the technical indices of oil and gold prices are calculated
as the independent variable and the closing price as the dependent variable of the forecasting
model.
Findings – The findings indicate that there is no obvious correlation between the price tendencies of
oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better
forecast ability than the other three forecasting models.
Originality/value – This paper explored the correlation between oil and gold prices, and built oil and
gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an
escape parameter D is added to Si. The findings showed that the forecasting model that combines MFOA
and GRNN has the best ability to forecast the closing price of oil and gold.
Keywords Information technology, Algorithms, Optimization techniques, Economics, Modelling,
Artificial intelligence
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