Forecasting stock market volatility presents significant challenges and opportunities for both practitioners and researchers in the financial sector. This paper explores the application of eXtreme ...

Abstract Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health ...

The increasing demand for accurate energy consumption forecasting has spurred a surge of interest in leveraging advanced machine learning techniques. This project focuses on the application in XG ...

Time series forecasting for networking datasets using the binary model XGBoost. In this, we first make a main model for the dataset. After this, we conduct beta testing on the model by the means of a ...

Skyline Hospital Time Series Forecasting with XGBoost Image Source: Facebook Page This is a data analysis project that aimed to forecast future patient arrivals data on the outpatient unit of Skyline ...

Using XGBoost for time-series analysis can be considered as an advance approach of time series analysis. this approach also helps in improving our results and speed of modelling.