Accession Number:

ADA094315

Title:

Time Series Model Identification and Prediction Variance Horizon.

Descriptive Note:

Technical rept.,

Corporate Author:

TEXAS A AND M UNIV COLLEGE STATION INST OF STATISTICS

Personal Author(s):

Report Date:

1980-06-01

Pagination or Media Count:

35.0

Abstract:

An approach to time series modelling is described it classifies the time into one of three memory types called no memory, short memory, and long memory, and then finds a whitening filter. When the time series is short memory one would like to identify the whitening filter type as AR, MA, or ARMA before parameter estimation. A new tool is introduced which can be used to diagnose both the memory type of a time series, and the whitening filter type of a short memory time series. It is called prediction variance horizon function. To classify the model type of a time series, one uses the shape of PVH and the value of the horizon HOR defined as the smallest value of h for which PVHh less than or 0.05. The analysis of a real time series, called Freeze, is described.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE