Mekon J.Wongsuwarn H.Radeerom M.Vephasayanant A.Supadol T.Songmuang P.Mahidol University2026-04-012026-04-012025-01-012025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025)https://repository.li.mahidol.ac.th/handle/123456789/115931Discontinuities in financial time-series data caused by market holidays can significantly affect the accuracy of predictive models. This study explores interpolation strategies to address holiday-induced gaps in daily stock prices of eight major NASDAQ technology companies (2019-2024), using data from Yahoo Finance. Three approaches-linear interpolation, nearest interpolation, and no interpolation-were compared using machine learning models to forecast next-day closing prices. Input features included 7-day lagged price data and technical indicators such as SMA, EMA, and Bollinger Bands. Model performance was evaluated using MAE and R². Results show that linear interpolation consistently yielded the lowest mean MAE, reducing error by 23.9% compared to no interpolation, and improved mean R² by 1.15%, demonstrating higher explanatory power. Linear interpolation outperformed both alternative methods for every company examined, highlighting its substantial benefit for forecasting accuracy and model robustness in financial time-series prediction.Computer ScienceEngineeringConference PaperSCOPUS10.1109/iSAI-NLP66160.2025.113207132-s2.0-105032732063