Time Series and Forecasting

A time series is a sequence of observations collected sequentially over time. Time series arise in economics, business, engineering, social sciences and the natural sciences. The main feature of a time series is that adjacent observations tend to be serially dependent (correlated). Time series analysis is aimed at explaining this dependence with descriptive methods and statistical models. Once a model is found and fitted to data it provides a concise summary of the main characteristics of a time series, such as trends and seasonal variations, which can be essential for decision makers. A fitted model can be used to forecast future values of a time series from current and past values.

We can assist clients with:

• building time series models, and

• developing effective forecasting systems.

SOME RECOMMENDED RESOURCES

• Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. 1994. Time Series Analysis: Forecasting and Control. Prentice-Hall International: New Jersey.

• Cowpertwait, P.S.P. and Metcalfe, A.V. 2009. Introductory Time Series with R. Springer: New York.

• Hyndman, R.J., Koehler, A.B., Ord, J.K. Snyder, R.D. 2008 . Forecasting with Exponential Smoothing: The State Space Approach. Springer: Heidelberg.

• Jenkins, G.M. 1979. Practical Experiences with Modelling and Forecasting Time Series. Gwilym Jenkins and Partners: St Helier.