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：本研究提出了一种新颖的预测策略，通过利用分数差分（FD）的强大能力，捕捉时间序列数据中的短期和长期依赖关系。与传统的整数差分方法不同，FD在保持序列稳定性的同时保留了其记忆特性，从而适用于建模。通过对SPY指数的金融数据应用FD，并结合新闻报告的情感分析，本实证研究探索了FD与目标变量二分类结合的有效性。采用监督分类算法验证了FD序列的性能。结果表明，FD优于整数差分方法，这一结论通过接收者操作特征曲线下面积（ROCAUC）和马修斯相关系数（MCC）评估得到了证实。