https://github.com/allrivertosea/stock_return_forecast_based_on_pso_flnn
This section is used to predict weekly stock returns, where predicted returns replace historical returns to calculate statistical metrics such as the mean return vector, variance, and skewness. This provides a data foundation for constructing a prediction-based tri-objective model in the study.
https://github.com/allrivertosea/stock_return_forecast_based_on_pso_flnn
Science Score: 13.0%
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Low similarity (1.9%) to scientific vocabulary
Repository
This section is used to predict weekly stock returns, where predicted returns replace historical returns to calculate statistical metrics such as the mean return vector, variance, and skewness. This provides a data foundation for constructing a prediction-based tri-objective model in the study.
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README.md
StockreturnforecastbasedonPsoflnn
This section is used to predict weekly stock returns, where predicted returns replace historical returns to calculate statistical metrics such as the mean return vector, variance, and skewness. This provides a data foundation for constructing a prediction-based tri-objective model in the study.
利用粒子群优化的函数链神经网络预测股票收益
该部分用来预测股票的周收益，用预测收益替代历史收益计算收益均值向量、方差和偏度等统计量，为研究中构建基于预测的三目标模型打下数据基础。
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- Login: allrivertosea
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- Profile: https://github.com/allrivertosea
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