Gold Price Fluctuation Forecasting Based on Newton and Lagrange Polynomial Interpolation
Abstract
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 2
nd 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 2
nd order polynomial interpolation which are smaller than in 3
rd
, 5
th
, 6
th, and 10th order
polynomial interpolation. This suggests that a polynomial of 2
nd 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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