619-360 Time Series and Forecasting

Note:

Credit Points:

Prerequisite/s:

Timetable:

Contact:

Objectives:

Students completing this course should:

Comprehend:

• the concept of a time series;

• the usefulness of time series analysis;

• the basic principles of time series analysis in the time domain and in the frequency domain;

• simple models for time series.

Have developed the skills:

• to use standard forecasting techniques;

• to carry out simple time series analyses in the time domain and in the frequency domain;

• to use the computer as an aid to efficient analysis of time series.

Appreciate:

• the patterns inherent in real-world time series;

• the importance of the underlying mathematical theory of time series;

• the application of computer software in the analysis of time series.

Content:

Patterns in time series. Simple methods for exploratory data analysis: smoothing techniques. Smoothing and decomposition. Trends and seasonal variation. Simple forecasting methods: mean, last value. Accuracy of forecasts. Exponential methods. Models for time series: stationarity, autocorrelation; ARMA processes; ARIMA processes. Estimation and model fitting. Spectral analysis. Smoothing by Fourier series. Linear filtering.

Assessment: