Trend-Based Encoding of Exogenous Time-Series for Interpretable Financial Prediction

Abstract: Integrating heterogeneous exogenous data into financial prediction models is challenging due to scale mismatches and semantic ambiguity. We propose a trend-encoding framework that transforms raw exogenous time-series into directional binary representations, improving predictive robustness while preserving interpretability. Using Saudi stock market data with COVID- 19 indicators, we evaluate predictive models under baseline and trend-enhanced configurations. Results show that trend encoding consistently enhances predictive stability over raw inputs. Interpretable models benefit disproportionately, achieving performance comparable to black-box methods. Sectoral analysis reveals heterogeneous sensitivities: Banking responds strongly to case and mortality trends, Energy to recovery indicators, while Food & Beverages shows weaker alignment. These findings show that trend-based encoding of exogenous signals can improve cross-domain financial prediction, particularly for interpretable models.

Khudran M. Alzhrani. “Trend-Based Encoding of Exogenous Time-Series for Interpretable Financial Prediction”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170394

@article{Alzhrani2026,
title = {Trend-Based Encoding of Exogenous Time-Series for Interpretable Financial Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170394},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170394},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Khudran M. Alzhrani}
}