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ASP Statistical Software
A STATISTICAL PACKAGE (ASP)

For Business, Economics, And The Social Sciences


 

Time Series

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Time Series Models

There are nine time series models available in ASP:

  • Naive Forecast.
  • Simple Moving Average.
  • Simple Exponential Smoothing.
  • Linear Exponential Smoothing.
  • Seasonal Exponential Smoothing.
  • Adaptive Filtering.
  • Seasonal Decomposition.
  • ARIMA Model.

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Basic Time Series Output

Time series output is generated and displayed in two stages:

  1. First, a table of summary statistics is generated and displayed.
  2. You are then given option for generating additional output from the model being estimated.

Summary Statistics. Statistics summarizing the model are presented in a window at the top of the screen. These statistics include:

  • Mean error.
  • Percent error.
  • Absolute error.
  • Absolute percent error.
  • Squared error for the estimated forecast.
  • Next period forecast.

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Additional Time Series Output

Once a model is estimated you have five additional kinds of output you can generate:

Output Matrix. ASP generates an output matrix for each model you estimate. This matrix contains the actual value of the variable being forecasted along with its forecasted value for each period and the amount of error in each forecast. There may be other information that is specific to a particular forecasting routine. You are given the option of adding the data in this matrix to the data matrix in memory.

Error Plot. You can plot the forecast errors of the model being estimated.

N Period Forecast. You can use the model being estimated to forecast N periods into the future.

Error Correlation Plot. You can plot the autocorrelations and the partial autocorrelations of the errors of the model being estimated.

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