Gold Price Forecasting using Regression Techniques for Settling Economic and Stock Market Inconsistency

Autor: Riajuliislam, Abdus Sattar, Ruhul Amin Razu, Shampa Islam Momo, Mossadek Ali Mithu, Kazi Motiour Rahman
Rok vydání: 2021
Předmět:
Zdroj: ICCCNT
DOI: 10.1109/icccnt51525.2021.9579755
Popis: Gold can be titled as one of the metals of utmost prominence to a country. It is a regulatory factor for financial banks and the stock exchange. Gold can have an immense influence on the economic sector. High fluctuation rate of gold price very common scene in almost all countries. Our country Bangladesh is no exception. As it is kept as a reserve by the central bank, variation in its price can cause complications in the economy of the country. In this work, we have proposed our models to predict the daily price of gold. We have used Support Vector Regression (SVR), Random Forest Regressor (RFR), Decision Tree, Gradient Boosting, and XGBoost models to forecast the daily gold price.
Databáze: OpenAIRE