Stochastic Modelling (Time Series Analysis) and Forecasting



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).



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