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）的强大能力来捕捉时间序列数据中的短期与长期依赖性。与传统整数差分方法不同，FD在稳定序列以便于建模的同时，保留了序列中的记忆特性。通过将FD应用于SPY指数的金融数据，并结合新闻报道的情感分析，本实证研究探讨了FD与目标变量二分类结合的有效性。采用监督分类算法来验证FD序列的性能。结果表明，FD优于整数差分，这一结论得到了受试者工作特征曲线下面积（ROCAUC）和马修斯相关系数（MCC）评估的确认。