This study introduces a novel forecasting strategy that leverages the power of fractional differencing (FD) to capture both short- and long-term dependencies in time series data. Unlike traditional integer differencing methods, FD preserves memory in series while stabilizing it for modeling purposes. By applying FD to financial data from the SPY index and incorporating sentiment analysis from news reports, this empirical analysis explores the effectiveness of FD in conjunction with binary classification of target variables. Supervised classification algorithms were employed to validate the performance of FD series. The results demonstrate the superiority of FD over integer differencing, as confirmed by Receiver Operating Characteristic/Area Under the Curve (ROCAUC) and Mathews Correlation Coefficient (MCC) evaluations.

翻译：本研究提出一种新颖的预测策略，通过利用分数阶差分的力量来捕捉时间序列数据中的短期和长期依赖关系。与传统的整数阶差分方法不同，分数阶差分在稳定序列以进行建模的同时，保留了序列中的记忆。通过将分数阶差分应用于SPY指数的金融数据，并结合新闻报告中的情感分析，本实证研究探讨了分数阶差分与目标变量二分类相结合的有效性。采用监督分类算法验证了分数阶差分序列的性能。结果表明，分数阶差分优于整数阶差分，这一点通过接收者操作特征曲线下面积和Matthews相关系数评估得到了证实。