ABSTRACT
The analysis of environmental data is inherently complex, with datasets often containing nonlinearities and temporal, spatial, and seasonal trends, as well as non-Gaussian distributions. Time series analysis attempts to address these concerns through the following operations:
1. the identifi cation of the phenomenon represented by sequences of observations;
2. the characterization of patterns in a given dataset; 3. forecasting or prediction of future values of a given time series variable or
set of variables.
