Time Series Interpolation of Holiday Gaps for Enhanced Stock Price Prediction

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 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.