The analysis of time series is based on
the assumption that successive values in the data file represent consecutive
measurements taken at equally spaced time intervals. There are two main
goals of time series analysis: (a) identifying the nature of the phenomenon
represented by the sequence of observations, and (b) forecasting (predicting
future values of the time series variable). Both of these goals require that
the pattern of observed time series data is identified and more or less
formally described. Once the pattern is established, we can interpret and
integrate it with other data (i.e., use it in our theory of the investigated
phenomenon, e.g., seasonal commodity prices). Regardless of the depth of our
understanding and the validity of our interpretation (theory) of the
phenomenon, we can extrapolate the identified pattern to predict future
events.
