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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 5, Issue 3) |
| DOI | 10.11648/j.ajtas.20160503.16 |
| Page(s) | 115-122 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Innovative Outlier, Additive Outlier, Multiplicative Outlier, Vector Auto Regressive
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APA Style
Olufolabo Olusesan Oluyomi., Shittu Olarenwaju Ismail., Adepoju Kazeem Adesola. (2016). Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series. American Journal of Theoretical and Applied Statistics, 5(3), 115-122. https://doi.org/10.11648/j.ajtas.20160503.16
ACS Style
Olufolabo Olusesan Oluyomi.; Shittu Olarenwaju Ismail.; Adepoju Kazeem Adesola. Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series. Am. J. Theor. Appl. Stat. 2016, 5(3), 115-122. doi: 10.11648/j.ajtas.20160503.16
AMA Style
Olufolabo Olusesan Oluyomi., Shittu Olarenwaju Ismail., Adepoju Kazeem Adesola. Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series. Am J Theor Appl Stat. 2016;5(3):115-122. doi: 10.11648/j.ajtas.20160503.16
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Download@article{10.11648/j.ajtas.20160503.16,
author = {Olufolabo Olusesan Oluyomi. and Shittu Olarenwaju Ismail. and Adepoju Kazeem Adesola.},
title = {Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {5},
number = {3},
pages = {115-122},
doi = {10.11648/j.ajtas.20160503.16},
url = {https://doi.org/10.11648/j.ajtas.20160503.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160503.16},
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.},
year = {2016}
}
TY - JOUR T1 - Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series AU - Olufolabo Olusesan Oluyomi. AU - Shittu Olarenwaju Ismail. AU - Adepoju Kazeem Adesola. Y1 - 2016/05/10 PY - 2016 N1 - https://doi.org/10.11648/j.ajtas.20160503.16 DO - 10.11648/j.ajtas.20160503.16 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 115 EP - 122 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20160503.16 AB - 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. VL - 5 IS - 3 ER -
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