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.

本研究介绍了一种新的预测策略，利用分数差分（FD）的力量捕捉时间序列数据中的短期和长期依赖关系。通过将 FD 应用于 SPY 指数的金融数据，并结合来自新闻报道的情绪分析，本实证分析探讨了 FD 与目标变量的二分类的有效性。使用监督分类算法验证了 FD 序列的性能，结果表明 FD 相较于整数差分具有优势，这一结果通过接收者操作特征 / 曲线下面积（ROCAUC）和马修斯相关系数（MCC）评估得到确认。