The Box-Jenkins methodology is a systematic approach used for time series forecasting. Developed by statisticians George E. P. Box and Gwilym M. Jenkins, it focuses on identifying, estimating, and diagnosing models that can predict future values based on past data. The process typically involves three main steps: model identification, parameter estimation, and model diagnostics. This methodology primarily utilizes ARIMA (AutoRegressive Integrated Moving Average) models, which combine autoregressive and moving average components. By analyzing patterns in historical data, Box-Jenkins helps in making informed predictions, making it a valuable tool in various fields such as economics, finance, and environmental science.