Time Series Analysis 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.
  • Makridakis, S.G., Wheelwright, S.C. and Hyndman, R.J. 1998. Forecasting: Methods and Applications. Third edition. John Wiley and Sons.