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 ...

This paper discusses the production problem of multi-variety materials. With the continuous development of economic globalization, the production and operation mode of many varieties of small batch ...

This repository contains the code for time-series forecasting using various models, such as InceptionTime, LSTM, XGBoost, etc. It contains the complete pipeline from data preprocessing, model building ...

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.

Choi and Hur, (2020) use random forest (RF), XGBoost, and LightGBMs as ensemble models to forecast photovoltaic power. Besides, the forecasting objective can be grouped to wind turbine, single wind ...