Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. Methods applied in digital signal processing can be applied to stock data as both are time series. Similarly, learning outcome of this paper can be applied to speech time series data. Deep learning for stock prediction has been introduced in this paper and its performance is evaluated on Google stock price multimedia data (chart) from NASDAQ. The objective of this paper is to demonstrate that deep learning can improve stock market forecasting accuracy. For this, (2D)²PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D)²PCA + Radial Basis Function Neural Network (RBFNN). It is found that the proposed method is performing better than the existing method RBFNN with an improved accuracy of 4.8% for Hit Rate with a window size of 20. Also the results of the proposed model are compared with the Recurrent Neural Network (RNN) and it is found that the accuracy for Hit Rate is improved by 15.6%. The correlation coefficient between the actual and predicted return for DNN is 17.1% more than RBFNN and it is 43.4% better than RNN.

中文翻译：

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使用深度学习进行库存预测

股市被认为是混乱，复杂，波动和动态的。毫无疑问，其预测是时间序列预测中最具挑战性的任务之一。此外，现有的人工神经网络（ANN）方法无法提供令人鼓舞的结果。同时，机器学习的进步为语音识别，图像分类和语言处理提供了良好的结果。数字信号处理中应用的方法可以应用于股票数据，因为两者都是时间序列。同样，本文的学习成果可以应用于语音时间序列数据。本文介绍了用于股票预测的深度学习，并根据纳斯达克的Google股票价格多媒体数据（图表）对它的性能进行了评估。本文的目的是证明深度学习可以提高股市预测的准确性。^将2 PCA +深度神经网络（DNN）方法与最新方法进行比较。二维二维主成分分析（2D）² PCA +径向基函数神经网络（RBFNN）。发现所提出的方法比现有方法RBFNN的性能更好，窗口大小为20的命中率的准确率提高了4.8％。还将所提出模型的结果与递归神经网络（RNN）和发现命中率的准确性提高了15.6％。DNN的实际收益与预测收益之间的相关系数比RBFNN高17.1％，比RNN高43.4％。