高效股票的超启发式优化前馈神经网络

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

英文标题：
《Metaheuristics optimized feedforward neural networks for efficient stock
  price prediction》
---
作者：
Bradley J. Pillay and Absalom E. Ezugwu
---
最新提交年份：
2020
---
英文摘要：
  The prediction of stock prices is an important task in economics, investment and making financial decisions. This has, for decades, spurred the interest of many researchers to make focused contributions to the design of accurate stock price predictive models; of which some have been utilized to predict the next day opening and closing prices of the stock indices. This paper proposes the design and implementation of a hybrid symbiotic organisms search trained feedforward neural network model for effective and accurate stock price prediction. The symbiotic organisms search algorithm is used as an efficient optimization technique to train the feedforward neural networks, while the resulting training process is used to build a better stock price prediction model. Furthermore, the study also presents a comparative performance evaluation of three different stock price forecasting models; namely, the particle swarm optimization trained feedforward neural network model, the genetic algorithm trained feedforward neural network model and the well-known ARIMA model. The system developed in support of this study utilizes sixteen stock indices as time series datasets for training and testing purpose. Three statistical evaluation measures are used to compare the results of the implemented models, namely the root mean squared error, the mean absolute percentage error and the mean absolution deviation. The computational results obtained reveal that the symbiotic organisms search trained feedforward neural network model exhibits outstanding predictive performance compared to the other models. However, the performance study shows that the three metaheuristics trained feedforward neural network models have promising predictive competence for solving problems of high dimensional nonlinear time series data, which are difficult to capture by traditional models.
---
中文摘要：
股票价格预测是经济学、投资学和财务决策学中的一项重要任务。几十年来，这激发了许多研究人员的兴趣，他们致力于设计准确的股价预测模型；其中一些被用来预测股票指数的次日开盘价和收盘价。本文提出了一种混合共生生物搜索训练的前馈神经网络模型的设计与实现，用于有效而准确的股价预测。将共生生物搜索算法作为一种有效的优化技术来训练前馈神经网络，并将其训练过程用于构建更好的股价预测模型。此外，本研究还对三种不同的股价预测模型进行了绩效比较评估；即粒子群优化训练的前馈神经网络模型、遗传算法训练的前馈神经网络模型和著名的ARIMA模型。为支持本研究而开发的系统利用16种股票指数作为时间序列数据集，用于培训和测试目的。使用三种统计评估指标，即均方根误差、平均绝对百分比误差和平均绝对偏差，来比较所实现模型的结果。计算结果表明，与其他模型相比，共生生物搜索训练的前馈神经网络模型具有优异的预测性能。然而，性能研究表明，三种元启发式训练的前馈神经网络模型对于解决传统模型难以捕捉的高维非线性时间序列数据问题具有很好的预测能力。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--
一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
PDF下载：
-->

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：神经网络 神经网 启发式 Applications Optimization