The Box-Jenkins Method is a systematic approach used for time series forecasting. It involves identifying, estimating, and diagnosing models that can explain the underlying patterns in historical data. The method primarily focuses on ARIMA (AutoRegressive Integrated Moving Average) models, which help in capturing trends and seasonality in the data. This technique consists of three main steps: model identification, parameter estimation, and model diagnostics. By analyzing the autocorrelation and partial autocorrelation functions, analysts can determine the appropriate model structure. Once a model is fitted, it is evaluated for accuracy to ensure reliable forecasts for future data points.