This section provides more information about classic time-series forecasting methods used in Predictor and described in Using Classic Time-series Forecasting Methods. Information about ARIMA (also known as Box-Jenkins) time-series forecasting methods are found in Using ARIMA Time-series Forecasting Methods.
Time-series forecasting assumes that historical data is a combination of a pattern and some random error. Its goal is to isolate the pattern from the error by understanding the pattern’s level, trend, and seasonality. You can then measure the error using a statistical measurement to describe both how well a pattern reproduces historical data and to estimate how accurately it projects the data into the future. See Time-series Forecasting Accuracy Measures.
By default, Predictor tries all of the classic time-series methods in the Methods Gallery. It then ranks them according to which method has the lowest error, depending on the error measure selected in the Options pane. The method with the lowest error is the best method.
There are two primary techniques of classic time-series forecasting used in Predictor:
Classic Non-seasonal Forecasting Method Parameters — Estimate a trend by removing extreme data and reducing data randomness
Classic Seasonal Forecasting Methods — Combine forecasting data with an adjustment for seasonal behavior
For information about regression forecasting methods, see Multiple Linear Regression. See the Oracle Crystal Ball Statistical Guide for more information about the formulas Predictor uses for the non-seasonal and seasonal forecasting methods described in the following sections.