A Novel Approach for Correlation Analysis on FBProphet to Forecast Market Gold Rates with Linear Regression

Abstract

This work aims to forecast gold prices for future dates using FBprophet and Linear Regression. For predicting the gold price using Linear Regression with a sample size of 140, FBprophet for time series analysis was suggested. The Dickey-Fuller test extracts seasonality (non-stationary) data and converts it to static data. The accuracy of FBProphet is 97.2 percent, compared to 85.6 percent for linear regression. Compared to linear regression, FBProphet tends to do substantially better than linear regression, with a significance level of (p<0.05). FBProphet can help predict the percentage of gold rate with greater precision.