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Time Series Models and Mathematica

  • Robert A. Stine

Abstract

This notebook introduces a package of Mathematica functions that manipulate autoregressive, integrated moving average (ARIMA) models. ARIMA models describe discrete-time stochastic processes—time series. The models are most adept at modeling stationary processes. Through differencing, however, these models accommodate certain forms of nonstationary processes as well.

Keywords

Transfer Function Spectral Density Unit Circle Time Series Model ARMA Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1993

Authors and Affiliations

  • Robert A. Stine

There are no affiliations available

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