Box-Jenkins model

The Box-Jenkins model is a method used to analyze and forecast time series data—like sales, weather, or stock prices—by identifying patterns and relationships within the data. It involves three main steps: explaining current data with mathematical models, estimating the model parameters, and then using the model to make predictions. The approach emphasizes understanding the data’s structure, such as trends or seasonal patterns, to improve accuracy. Essentially, it helps uncover the underlying dynamics of data sequences to project future values more reliably.