论文标题
财务时间序列数据扩展，具有生成对抗网络和扩展的跨期返回图
Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots
论文作者
论文摘要
数据增强是一种关键的正规化方法，用于支持计算机视觉中高度参数化模型的预测和分类性能。但是，在时间序列域中，即使这些方法已证明可以减轻小样本量或非平稳性的影响，但在增强方面的正则化并不常见。在本文中，我们将基于最先进的基于图像的生成模型应用于数据增强的任务，并引入了扩展的跨期返回图（XIRP），这是时间序列的新图像表示。进行了多项测试，以评估增强技术的质量，以有效地合成时间序列并改善M4竞争子集的预测结果。我们进一步研究了数据集特征和采样结果之间通过Shapley值的功能归因于性能指标以及增强数据的最佳比率。在所有数据集中，我们的方法被证明可以有效地减少79％的财务数据集的回报预测错误，并具有不同的统计属性和频率。
Data augmentation is a key regularization method to support the forecast and classification performance of highly parameterized models in computer vision. In the time series domain however, regularization in terms of augmentation is not equally common even though these methods have proven to mitigate effects from small sample size or non-stationarity. In this paper we apply state-of-the art image-based generative models for the task of data augmentation and introduce the extended intertemporal return plot (XIRP), a new image representation for time series. Multiple tests are conducted to assess the quality of the augmentation technique regarding its ability to synthesize time series effectively and improve forecast results on a subset of the M4 competition. We further investigate the relationship between data set characteristics and sampling results via Shapley values for feature attribution on the performance metrics and the optimal ratio of augmented data. Over all data sets, our approach proves to be effective in reducing the return forecast error by 7% on 79% of the financial data sets with varying statistical properties and frequencies.
