Box-Jenkins Method

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What is the Box-Jenkins Methodology?

The Box-Jenkins Methodology is a statistical process used to generate forecasts from time-series data and analyze the results. It is an iterative approach that involves identifying a process that can be approximated by a model, estimating its parameters, and diagnostically checking the results. This methodology allows for the prediction of future events or understanding of past events. The three steps in this process are identification, estimation, and diagnostic checking which help improve the accuracy of the model with each iteration. Additionally, it can be used to measure and improve data quality by measuring variations between datasets.

See Also

  1. ARIMA (Autoregressive Integrated Moving Average)
  2. Seasonal Decomposition of Time Series (STL)
  3. Autocorrelation and Partial Autocorrelation Functions (ACF and PACF)
  4. Time Series Analysis
  5. SARIMA (Seasonal ARIMA)
  6. Stationarity and Differencing
  7. Forecasting
  8. Exponential Smoothing