Our life is surrounded by processes that are taken place in the time. The theory of stochastic processes deals with the modeling of these kind of processes. People discovered, that the observation of these procesess provides a lot of useful information in wide range of the life in the work in controlling and forecasting.
Time series can be applied in meteorology, where they are used in weather forecast, and in economic analysis, because the stock indexes, yearly GDP, the emission of industrial goods, and the current rates can be typified by time series too. They can be used in agricultural and medical analysis and in psychology. The prospective growth of wheat, and the risk of outbreak of epidemics can be estimated by them. Time series are useful in evaluation of datas, provided by medical instruments, like PET, ECG, or automatization of controlling systems.
Nowadays there are numerous gorgeous computer softwares are available to work with time series datas. These softwares are BMDP, GRETL, SAS, and the Forecasting module of the IBM SPSS Statistics. In my thesis I show the possibilities of usage of this latter programme, IBM SPSS Statistics on real-life financial process analysis.