The Box-Jenkins Methodology is a systematic approach used for time series forecasting. It involves three main steps: model identification, parameter estimation, and model diagnostics. This methodology helps in selecting the appropriate statistical model to analyze and predict future values based on past data. Developed by statisticians George Box and G. E. P. Box, the methodology primarily focuses on ARIMA (AutoRegressive Integrated Moving Average) models. It emphasizes the importance of understanding the underlying patterns in the data, such as trends and seasonality, to improve forecasting accuracy.