Time series analysis is based on the type of data where a variable is regularly measured in time. The method is primarily used to:

  • Decompose time series. For example, seasonal adjustment
  • Identify and model the systematic variation
  • Identify and model the time-based dependencies
  • Forecasts

Nowadays, the so-called Box-Jenkins models are perhaps the most commonly used and many techniques used for forecasting and seasonal adjustment can be traced back to these models.

Another line of development are non-linear generalizations, mainly ARCH (AutoRegressive Conditional Heteroscedasticity) - and GARCH- (G = Generalized) models which have proved very useful, especially for financial time series. The invention of them and the release of a way to correct the models for errors, provided C. W. J. Granger and R. F. Engle with the Nobel Memorial Prize in Economic Sciences in 2003.

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