Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as ...

🎬 Project Overview Time series analysis of actor career trajectories using ARIMA models to forecast future IMDb ratings based on historical performance data.

In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for ...

This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, ...

This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and ...

Finally, Sharma et al. (2021) proposed a new method for time-series forecasting of nonstationary data using a combination of EVDHM and ARIMA models. Although these studies have shown promising results ...

Time series forecasting using historical data is significantly important nowadays. Many fields such as finance, industries, healthcare, and meteorology use it. Profit analysis using financial data is ...