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Time series forecasting of the number of Malaysia Airlines and AirAsia passengers

The standard practice in forecasting process involved by fitting a model and further analysis on the residuals. If we know the distributional behaviour of the time series data, it can help us to directly analyse the model identification, parameter estimation, and model checking. In this paper, we wa...

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Main Authors: Asrah, N. M., Nor, M. E., Rahim, S. N. A., Leng, W. K.
Format: Article
Language:English
Published: IOP Publishing 2018
Subjects:
HE9761-9900 Air transportation. Airlines
Online Access:http://eprints.uthm.edu.my/5695/1/AJ%202018%20%28310%29.pdf
http://eprints.uthm.edu.my/5695/
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spelling my.uthm.eprints.56952022-01-20T06:40:07Z http://eprints.uthm.edu.my/5695/ Time series forecasting of the number of Malaysia Airlines and AirAsia passengers Asrah, N. M. Nor, M. E. Rahim, S. N. A. Leng, W. K. HE9761-9900 Air transportation. Airlines The standard practice in forecasting process involved by fitting a model and further analysis on the residuals. If we know the distributional behaviour of the time series data, it can help us to directly analyse the model identification, parameter estimation, and model checking. In this paper, we want to compare the distributional behaviour data from the number of Malaysia Airlines (MAS) and AirAsia passenger’s. From the previous research, the AirAsia passengers are govern by geometric Brownian motion (GBM). The data were normally distributed, stationary and independent. Then, GBM was used to forecast the number of AirAsia passenger’s. The same methods were applied to MAS data and the results then were compared. Unfortunately, the MAS data were not govern by GBM. Then, the standard approach in time series forecasting will be applied to MAS data. From this comparison, we can conclude that the number of AirAsia passengers are always in peak season rather than MAS passengers. IOP Publishing 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5695/1/AJ%202018%20%28310%29.pdf Asrah, N. M. and Nor, M. E. and Rahim, S. N. A. and Leng, W. K. (2018) Time series forecasting of the number of Malaysia Airlines and AirAsia passengers. Journal of Physics: Conference Series, 995. pp. 1-15. 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 HE9761-9900 Air transportation. Airlines
spellingShingle HE9761-9900 Air transportation. Airlines
Asrah, N. M.
Nor, M. E.
Rahim, S. N. A.
Leng, W. K.
Time series forecasting of the number of Malaysia Airlines and AirAsia passengers
description The standard practice in forecasting process involved by fitting a model and further analysis on the residuals. If we know the distributional behaviour of the time series data, it can help us to directly analyse the model identification, parameter estimation, and model checking. In this paper, we want to compare the distributional behaviour data from the number of Malaysia Airlines (MAS) and AirAsia passenger’s. From the previous research, the AirAsia passengers are govern by geometric Brownian motion (GBM). The data were normally distributed, stationary and independent. Then, GBM was used to forecast the number of AirAsia passenger’s. The same methods were applied to MAS data and the results then were compared. Unfortunately, the MAS data were not govern by GBM. Then, the standard approach in time series forecasting will be applied to MAS data. From this comparison, we can conclude that the number of AirAsia passengers are always in peak season rather than MAS passengers.
format Article
author Asrah, N. M.
Nor, M. E.
Rahim, S. N. A.
Leng, W. K.
author_facet Asrah, N. M.
Nor, M. E.
Rahim, S. N. A.
Leng, W. K.
author_sort Asrah, N. M.
title Time series forecasting of the number of Malaysia Airlines and AirAsia passengers
title_short Time series forecasting of the number of Malaysia Airlines and AirAsia passengers
title_full Time series forecasting of the number of Malaysia Airlines and AirAsia passengers
title_fullStr Time series forecasting of the number of Malaysia Airlines and AirAsia passengers
title_full_unstemmed Time series forecasting of the number of Malaysia Airlines and AirAsia passengers
title_sort time series forecasting of the number of malaysia airlines and airasia passengers
publisher IOP Publishing
publishDate 2018
url http://eprints.uthm.edu.my/5695/1/AJ%202018%20%28310%29.pdf
http://eprints.uthm.edu.my/5695/
_version_ 1738581406195384320
score 13.211869

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