Multivariate Approach To Time Series Model Identification

ABSTRACT

This work suggests an exact and systematic model identification approach which is

entirely new and addresses most of the challenges of existing methods. We

developed quadratic discriminant functions for various orders of autoregressive

moving average (ARMA) models. An Algorithm that is to be used alongside our

functions was also developed. In achieving this, three hundred sets of time series

data were simulated for the development of our functions. Another twenty five sets

of simulated time series data were used in testing out the classifiers which correctly

classified twenty three out of the twenty five sets. The two cases of

misclassification merely imply that our Algorithm will require a second iteration to

correctly identify the model in question. The Algorithm was also applied to some

real life time series data and it correctly classified it in two iterations.