Forecasting gold market trends using Backward Newton’s Divided Difference interpolation

Accurate short-term forecasting plays a crucial role in decision-making across various domains, particularly in financial markets. This study applies Newton’s Backwards Divided Difference (NBDD) interpolation method to predict gold price trends using recent historical data. NBDD is especially suitable for extrapolation near the end of a dataset, making it an effective tool for short-term forecasting. Polynomial models of degree 3 and degree 7 are constructed to study the prediction accuracy. The findings indicate that lower-degree polynomials provide a closer fit to historical data, while higher-degree polynomials effectively capture the general trend.