Trade value prediction (TVP) is major for understanding financial dynamics and directing policy decisions in the perspective of complex systems science. The study emphases on an analytical model intended to predict fu...
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Trade value prediction (TVP) is major for understanding financial dynamics and directing policy decisions in the perspective of complex systems science. The study emphases on an analytical model intended to predict future trade values by evaluating financial indicators, past trade data, and geopolitical powers. By using advanced statistical models and machine learning techniques, the model explores relationships and patterns in trade flows among countries. The perceptions increased from this technique offer beneficial supportfor policymakers and businesses, guiding them to forecast the effects of financial and policy changes on global trade. Also, the study emphasizes the importance of a complicated method to enhance the accuracy of trade predictions and aid tactical decision-making in a worldwide interconnected economy. This study proposes trade value prediction using hybrid graph convolutional recurrent neural network with lion optimizer algorithm (TVP-HGCRNNLOA) methodology. The objective function of the TVP-HGCRNNLOA methodology is to develop an accurate predictive model for trade values between countries. Primarily, the TVP-HGCRNNLOA approach undergoes the data normalization by employing linear scaling normalization technique. Then, the hybrid graph convolutional recurrent neural network (HGCRNN) method is used for forecasting process. At last, the TVP-HGCRNNLOA model performs the hyperparameter tuning by utilizing the lion optimization algorithm model. The experimental analysis of the TVP-HGCRNNLOA methodology is investigated in terms of various measures under mean squared error, mean absolute error, and mean absolute percentage error. The performance validation portrayed the superior performance of the TVP-HGCRNNLOA methodology over other existing approaches.
Grey wolf algorithm (GWO) is a classic swarm intelligence algorithm, but it has the disadvantages of slow convergence speed and easy to fall into local optimum on some problems. Therefore, an improved grey wolf optimi...
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Grey wolf algorithm (GWO) is a classic swarm intelligence algorithm, but it has the disadvantages of slow convergence speed and easy to fall into local optimum on some problems. Therefore, an improved grey wolf optimization algorithm(IGWO) is proposed. The lion optimizer algorithm and dynamic weights are integrated into the original grey wolf optimization algorithm. When the positions of alpha wolf, beta wolf, and delta wolf are updated, the lion optimizer algorithm is used to add disturbance factors to the wolves to give alpha wolf, beta wolf, and delta wolf active search capabilities. Dynamic weights are added to the grey wolf position update to prevent wolves from losing diversity and falling into local optimum. Through multiple benchmark function test experiments and path planning experiments, the experimental results show that the improved grey wolf optimization algorithm can effectively improve the accuracy and convergence speed, and the optimization effect is better.
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