Time series are considered as stochastic processes which are statistical phenomena that evolve over time. In the subsequent sections we discuss fundamental types of statistical models and some particular combinations:

• White noise models
• Random walk models
• Moving average (MA) models
• Autoregressive (AR) models
• Autoregressive moving average (ARMA) models
• Autoregressive integrated moving average (ARIMA) models

For further explanations of the specific models see for example the book Wilks2011.

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Citation

The E-Learning project SOGA-Py was developed at the Department of Earth Sciences by Annette Rudolph, Joachim Krois and Kai Hartmann. You can reach us via mail by soga[at]zedat.fu-berlin.de.

You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.