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|See also: ARIMA models, trends|
Setting up a model is a common approach to analyzing time series. Once a suitable model is found, it can be used for forecasting future time series elements. However, finding such a model is not straightforward. Typically, a standard model is chosen, and estimates of its parameters are determined based on a part of the data set. Then, its performance is checked on an independent test set. Since another model may provide better results, the original model is altered, its parameters are estimated, and the new model is also checked. This process of testing various models can be repeated until one of the models is accepted. If it models the time series satisfactorily, it may be applied to as yet unseen data.
To summarize, the following phases can be distinguished:
The figure below gives an overview of the model finding process:
Last Update: 2004-Jul-03