Time Series Interpolation of Holiday Gaps for Enhanced Stock Price Prediction
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105032732063
Journal Title
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025
Rights Holder(s)
SCOPUS
Bibliographic Citation
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025)
Suggested Citation
Mekon J., Wongsuwarn H., Radeerom M., Vephasayanant A., Supadol T., Songmuang P. Time Series Interpolation of Holiday Gaps for Enhanced Stock Price Prediction. 2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025). doi:10.1109/iSAI-NLP66160.2025.11320713 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115931
Title
Time Series Interpolation of Holiday Gaps for Enhanced Stock Price Prediction
Corresponding Author(s)
Other Contributor(s)
Abstract
Discontinuities 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 R<sup>2</sup>. Results show that linear interpolation consistently yielded the lowest mean MAE, reducing error by 23.9% compared to no interpolation, and improved mean R<sup>2</sup> 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.