Course number 419

Time Series Analysis with SPSS

Time series consist of observations made sequentially over time. They occur in a wide range of disciplines, ranging from business,
economics and social sciences to biomedical and engineering contexts. In analyzing time series one searches for structures and patterns
to describe and explain the underlying process and to forecast, based on adequate models fitted, future values or to predict results from alternative scenarios.
Apart from the ‘traditional’ methods for trend and seasonal decomposition of time series there are more advanced statistical techniques available for these tasks,
both in the time-domain (eg. ARMA-models) and in the frequency domain (eg. spectral and periodogram analyses).

In the CEE/e-course ‘Time Series forecasting with SPSS’ modern techniques associated with the analysis, modeling and forecasting of time series data
are discussed and underlying principles are explained.
Furthermore, the use of representative statistical software like SPSS is demonstrated and participants get the opportunity for hands-on experience
using SPSS for time series analysis and forecasting.

The following topics will be treated:

* Introduction and Overview.
* Models for Trend and Seasonal Decomposition.
* Exponential Smoothing Models.
* Box-Jenkins ARMA and ARIMA models.
* Analysis in the Frequency Domain: Spectral Analysis and
   Periodogram.
* Model Selection and Evaluation.
* Forecasting (both without and with predictor variables).
* Interpretation and Presentation of the Results.

After successful completion of the course participants should be able to analyze, model and forecast time series data with SPSS independently.