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.
, A & OCHE, J (2021). Multivariate Approach To Time Series Model Identification. Afribary. Retrieved from https://track.afribary.com/works/multivariate-approach-to-time-series-model-identification
, AGADA and JOSEPH OCHE "Multivariate Approach To Time Series Model Identification" Afribary. Afribary, 02 May. 2021, https://track.afribary.com/works/multivariate-approach-to-time-series-model-identification. Accessed 23 Nov. 2024.
, AGADA, JOSEPH OCHE . "Multivariate Approach To Time Series Model Identification". Afribary, Afribary, 02 May. 2021. Web. 23 Nov. 2024. < https://track.afribary.com/works/multivariate-approach-to-time-series-model-identification >.
, AGADA and OCHE, JOSEPH . "Multivariate Approach To Time Series Model Identification" Afribary (2021). Accessed November 23, 2024. https://track.afribary.com/works/multivariate-approach-to-time-series-model-identification