Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 3, May 2016, Pages: 115-122
Received: Apr. 5, 2016; Accepted: Apr. 25, 2016; Published: May 10, 2016
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Authors
Olufolabo Olusesan Oluyomi., Department of Statistics, Yaba College of Technology, Yaba, Nigeria
Shittu Olarenwaju Ismail., Department of Statistics, University of Ibadan, Ibadan, Nigeria
Adepoju Kazeem Adesola., Department of Statistics, University of Ibadan, Ibadan, Nigeria
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Abstract
This work is focused on developing two outlier generating mechanisms for the detection of outliers in the multivariate time series setting that is capable of ameliorating the swamping effect on regular observations in time series data. Specifying two-variable Vector Autoregressive (VAR) models and assuming innovative and multiplicative effect of outliers on time series data, the magnitude and variance of outlier were derived for the generating models by method of least squares. A modified test statistics were also developed to detect single outliers both in the response and explanatory variables. Real and simulated data were used to establish the validity of the models. The results show that the multiplicative is better than the additive model in terms of the number of outliers detected and the residual variance. This result is in line with previous studies in outlier detection in univariate time series.
Keywords
Innovative Outlier, Additive Outlier, Multiplicative Outlier, Vector Auto Regressive
To cite this article
Olufolabo Olusesan Oluyomi., Shittu Olarenwaju Ismail., Adepoju Kazeem Adesola., Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 3, 2016, pp. 115-122. doi: 10.11648/j.ajtas.20160503.16
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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