论文标题
金融时间序列分类的图像处理工具
Image Processing Tools for Financial Time Series Classification
论文作者
论文摘要
深度学习到时间序列预测的应用是当前机器学习的主要挑战之一。我们提出了一种新颖的方法，该方法将机器学习和图像处理方法结合在一起，以定义和预测市场状态和日内财务数据。小波变换应用于图像提取和降解的股票价格的记录。然后，一个卷积神经网络从DeNoed小波图像中提取模式，以对每日时间序列进行分类，即市场状态与基于每天最初几个小时的价格变化所构建的小波映像的每日近距离价格移动的二进制预测有关。该方法克服了财务时间序列中的低信噪比问题，并获得了市场状态“上升”和“向下”财务数据的竞争预测准确性。
The application of deep learning to time series forecasting is one of the major challenges in present machine learning. We propose a novel methodology that combines machine learning and image processing methods to define and predict market states with intraday financial data. A wavelet transform is applied to the log-return of stock prices for both image extraction and denoising. A convolutional neural network then extracts patterns from denoised wavelet images to classify daily time series, i.e. a market state is associated with the binary prediction of the daily close price movement based on the wavelet image constructed from the price changes in the first hours of the day. This method overcomes the low signal-to-noise ratio problem in financial time series and gets a competitive prediction accuracy of the market states 'Up' and 'Down' of financial data as tested on the S&P 500.
