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Treatment of outliers via interpolation method with neural network forecast performances

Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the sui...

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Main Authors: Wahir, N. A., Nor, M. E., Rusiman, M. S., Gopal, K.
Format: Article
Language:English
Published: IOP Publishing 2017
Subjects:
QC994.95-999 Weather forecasting
Online Access:http://eprints.uthm.edu.my/5684/1/AJ%202018%20%28305%29.pdf
http://eprints.uthm.edu.my/5684/
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spelling my.uthm.eprints.56842022-01-20T04:50:50Z http://eprints.uthm.edu.my/5684/ Treatment of outliers via interpolation method with neural network forecast performances Wahir, N. A. Nor, M. E. Rusiman, M. S. Gopal, K. QC994.95-999 Weather forecasting Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the suitable ways in treating outliers so as to ensure that the quality of the analyzed data is indeed high. As such, this paper discusses an alternative method to treat outliers via linear interpolation method. In fact, assuming outlier as a missing value in the dataset allows the application of the interpolation method to interpolate the outliers thus, enabling the comparison of data series using forecast accuracy before and after outlier treatment. With that, the monthly time series of Malaysian tourist arrivals from January 1998 until December 2015 had been used to interpolate the new series. The results indicated that the linear interpolation method, which was comprised of improved time series data, displayed better results, when compared to the original time series data in forecasting from both Box-Jenkins and neural network approaches. IOP Publishing 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/5684/1/AJ%202018%20%28305%29.pdf Wahir, N. A. and Nor, M. E. and Rusiman, M. S. and Gopal, K. (2017) Treatment of outliers via interpolation method with neural network forecast performances. Journal of Physics: Conference Series, 995. pp. 1-7. ISSN 1742-6588
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QC994.95-999 Weather forecasting
spellingShingle QC994.95-999 Weather forecasting
Wahir, N. A.
Nor, M. E.
Rusiman, M. S.
Gopal, K.
Treatment of outliers via interpolation method with neural network forecast performances
description Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the suitable ways in treating outliers so as to ensure that the quality of the analyzed data is indeed high. As such, this paper discusses an alternative method to treat outliers via linear interpolation method. In fact, assuming outlier as a missing value in the dataset allows the application of the interpolation method to interpolate the outliers thus, enabling the comparison of data series using forecast accuracy before and after outlier treatment. With that, the monthly time series of Malaysian tourist arrivals from January 1998 until December 2015 had been used to interpolate the new series. The results indicated that the linear interpolation method, which was comprised of improved time series data, displayed better results, when compared to the original time series data in forecasting from both Box-Jenkins and neural network approaches.
format Article
author Wahir, N. A.
Nor, M. E.
Rusiman, M. S.
Gopal, K.
author_facet Wahir, N. A.
Nor, M. E.
Rusiman, M. S.
Gopal, K.
author_sort Wahir, N. A.
title Treatment of outliers via interpolation method with neural network forecast performances
title_short Treatment of outliers via interpolation method with neural network forecast performances
title_full Treatment of outliers via interpolation method with neural network forecast performances
title_fullStr Treatment of outliers via interpolation method with neural network forecast performances
title_full_unstemmed Treatment of outliers via interpolation method with neural network forecast performances
title_sort treatment of outliers via interpolation method with neural network forecast performances
publisher IOP Publishing
publishDate 2017
url http://eprints.uthm.edu.my/5684/1/AJ%202018%20%28305%29.pdf
http://eprints.uthm.edu.my/5684/
_version_ 1738581404993716224
score 13.211869

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