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Stochastic Modelling (Time Series Analysis) and Forecasting

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Abstract

One of the objectives of statistical analysis of sequences of data is to draw inferences about the properties of the population from which these sequences of samples are drawn. Prediction of future observations is done by constructing relevant models based on stochastic process concepts. Stochastic processes can be classified as stationary and non-stationary. Special classes of linear models of stationary stochastic processes are:
  1. 1.

    Auto-regressive processes (AR),

     
  2. 2.

    Moving-average (MA) and

     
  3. 3.

    Auto-regressive and moving average processes (ARMA).

     

Keywords

Fast Fourier Transform Autocorrelation Function Time Series Analysis Autocorrelation Coefficient Copper Accumulation 
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

© Capital Publishing Company 2009

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