<![CDATA[Gold is a highly valuable commodity and an investment opportunity for many people. However, there are often significant fluctuations in gold prices that affect investment decisions. Various mathematical forecasting methods have been developed to anticipate gold price fluctuations. This study uses historical daily data of gold prices during January-May 2023. The method used in this study is the Newton and Lagrange polynomial interpolation method with several orders to analyze data and forecast gold price fluctuations. The purpose of this study is to compare the performance and accuracy of the order levels of the Newton and Lagrange polynomial interpolation forecasting models. In this study, the test data points and orders are selected so that a range is formed that matches the amount of data available. The test orders used in this study include orders 2, 3, 5, 6, and 10. This study found that the 2nd order polynomial interpolation method is more effective and accurate in forecasting gold price fluctuations compared to the higher orders tested. This is indicated by the results of the calculation of MAE, RMSE, and MAPE values in 2nd order polynomial interpolation which are smaller than in 3rd, 5th, 6th, and 10th order polynomial interpolation. This suggests that a polynomial of 2nd order has been able to model and forecast gold price fluctuations well. However, it is important to remember that these conclusions are based on the data and methods used in this study. Variability in forecasting results can occur depending on the quality of the data, the time period used, and the interpolation method applied, among others. Therefore, further research and wider testing needs to be conducted to validate these conclusions.]]>
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