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基于图像特征的财务时间序列集群

Jun Wu^1,2, Zelin Zhang^1,2, Rui Tong¹

¹School of Mathematics, Physics and Optical Engineering, Hubei University of Automotive Technology, Shi Yan, China.

|July 26, 2023

概括

本研究探讨了2D时间序列表示,以更好地分析数据. 经常性图表 (RP) 有效地识别金融市场的异常,在集群任务中表现优于其他方法.

科学领域:

数据科学数据科学数据科学
金融分析金融分析
时间序列分析时间序列分析

背景情况:

时间序列数据对于理解自然系统至关重要.
传统方法经常使用冗余因素,掩盖了潜在的模式.
更清晰的数据表示有助于识别关键驱动因素和预测系统变化.

研究的目的:

调查二维时间序列结构对数据表示和分析的有效性.
评估不同的2D方法在集群金融时间序列数据.
确定二维表示能否改善异常和趋势的识别.

主要方法:

使用了四种2D时间序列表示方法:反复图 (RP),格拉米安角总和场 (GASF),格拉米安角差分场 (GADF) 和马尔科夫过渡场 (MTF).
应用这些方法来分类CSI300和S&P500金融指数数据.
采用聚类技术,并使用轮值和不稳定性指标评估结果.

主要成果:

经常性图形 (RP) 图像显示在识别金融时间序列中的异常波动方面具有有效性,达到0.6到1.0之间的轮值.
与传统细分方法相比,2D模型表现出最低的不稳定性值 (0) .
从RP图像中提取的SIFT特征被证明是集群金融时间序列数据的优势,利用市场波动.

结论:

与传统方法相比,2D时间序列表示,特别是RP,为分析财务数据提供了一种优越的方法.
风险预测图像有效地捕获和突出显示财务时间序列中的异常,有助于风险评估和预测.
该研究验证了2D结构的实用性,用于增强从复杂的时间序列数据中获得的洞察力.

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