Time-Series Forecasting: Unleashing Long-Term Dependencies with Fractionally Differenced Data
This work addresses forecasting challenges in financial time series by improving dependency modeling, though it appears incremental as it builds on existing differencing methods with a specific adaptation.
This study tackled the problem of capturing long-term dependencies in time-series forecasting by introducing fractional differencing (FD) as a novel strategy, applied to SPY index financial data with sentiment analysis, and demonstrated its superiority over integer differencing with ROCAUC and MCC evaluations.
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.