基于长短期记忆算法的股票市场自动预测与Logistic回归的F1分数提高比较

P. Sairam, Logu. K
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引用次数: 1

摘要

这项工作提供了一个比较研究,提高F1分数的股票市场价值使用一种新的长短期记忆算法(LSTM),这是比较的逻辑回归算法。材料与方法:采用新颖长短期记忆算法($N=10$)和逻辑回归算法($N=10$)进行迭代,提高股市预测值的F1分数。通过不同的NLSTM和逻辑回归参数来模拟两种算法以优化ph。两组的样本量使用Gpower 80%计算,本工作中使用了20个样本。结果与讨论:LSTM的准确率(68.24%)明显优于logistic回归的准确率(53.71%)(0.407 ($p > 0.05$)。结论:长短期记忆算法有助于股票市场价格自动预测,提高F1分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Stock Market Prediction using Novel Long Short Term Memory Algorithm compared with Logistic Regression for improved F1 score
This work provides a comparative study of improved F1 score in stock market values using a novel long short term memory algorithm (LSTM) which is compared to Logistic Regression algorithm. Materials and Methods: Novel Long Short Term Memory ($N=10$) and logistic regression algorithm ($N=10$) were iterated to improve F1 score for stock market predicted values. Two algorithms are simulated by varying NLSTM and logistic regression parameters to optimize pH. Sample size is calculated using Gpower 80% for two groups and there are 20 samples used in this work. Results and Discussion: LSTM has notably better accuracy percentage (68.24%) compared to logistic regression accuracy (53.71%) with 0.407 ($p > 0.05$). Conclusion: Long short term memory algorithms help in predicting automatic stock market prices to improve F1 score.
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