基于梯度下降学习的人工神经网络金融时间序列预测比较研究
Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
摘要：
金融预测是信号处理问题的一个例子，由于小样本、高噪声、非平稳性和非线性，而股票市场价格的快速预测对于企业的战略规划是非常重要的，本研究的目的是建立一个比较预测模型，前馈多层人工神经网络，用于金融时间序列预测的递归时间延迟神经网络。这项研究是在GoogleFinance提供的历史股票价格数据集的帮助下开发的。为了开发这种预测模型，采用了具有梯度下降学习的反向传播方法。最后，神经网络，学习与所述算法被发现是熟练的预测非平稳噪声金融时间序列。
英文摘要：
Financial forecasting is an example of a signal processing problem which is challenging due to Small sample sizes, high noise, non-stationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice dataset made available by GoogleFinance.To develop this prediction model Backpropagation method with Gradient Descent learning has been implemented.Finally the Neural Net, learned with said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
