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Modeling positive time series data: a neglected aspect in time series courses

Something has been forgotten in time series courses, in particular, when dealing with positive datasets. To describe the pattern hidden in this type of datasets, before we use a sophisticated method of modeling such as Autoregressive Integrated Moving Average (ARIMA), we propose to check first wheth...

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Main Authors: Djauhari, M. A., Li, L. S., Salleh, R. M.
Format: Article
Published: Science Publications 2016
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/72330/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84985906551&doi=10.3844%2fajassp.2016.860.869&partnerID=40&md5=e4abbd328007e04897bc4dc55507bb5f
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spelling my.utm.723302017-11-23T04:17:44Z http://eprints.utm.my/id/eprint/72330/ Modeling positive time series data: a neglected aspect in time series courses Djauhari, M. A. Li, L. S. Salleh, R. M. QA Mathematics Something has been forgotten in time series courses, in particular, when dealing with positive datasets. To describe the pattern hidden in this type of datasets, before we use a sophisticated method of modeling such as Autoregressive Integrated Moving Average (ARIMA), we propose to check first whether the data represent a Geometric Brownian Motion (GBM) process. If it is affirmative, unlike the other methods, the method of GBM time series modeling might provide the desired model in a simple and easy to digest procedure with cheaper cost and high speed of computation. Because of its simplicity and practicality, even non-statisticians who have a very limited background in statistics could take easily the fruit and benefit of this method. In this study, unlike the standard approach that can be found in the literature, GBM process will be approached from log-normal process. This is the first result of this paper which shows the simplicity of GBM process. To identify this process, as a strong indication that a process is GBM process, we can see the value of the serial correlation. The smaller the serial correlation of log returns the higher the tendency that the process is GBM process. As the second result, for practical purposes, a new procedure of time series modeling if data are positive will be introduced. These results show that, when dealing with positive dataset, GBM time series modeling is worthwhile to be included in any introductory Time Series course especially for non-statistics students. To illustrate the practical advantages of GBM time series modeling, real case studies from industries as well as government agencies and internet will be presented and discussed. Science Publications 2016 Article PeerReviewed Djauhari, M. A. and Li, L. S. and Salleh, R. M. (2016) Modeling positive time series data: a neglected aspect in time series courses. American Journal of Applied Sciences, 13 (7). pp. 860-869. ISSN 1546-9239 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84985906551&doi=10.3844%2fajassp.2016.860.869&partnerID=40&md5=e4abbd328007e04897bc4dc55507bb5f
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Djauhari, M. A.
Li, L. S.
Salleh, R. M.
Modeling positive time series data: a neglected aspect in time series courses
description Something has been forgotten in time series courses, in particular, when dealing with positive datasets. To describe the pattern hidden in this type of datasets, before we use a sophisticated method of modeling such as Autoregressive Integrated Moving Average (ARIMA), we propose to check first whether the data represent a Geometric Brownian Motion (GBM) process. If it is affirmative, unlike the other methods, the method of GBM time series modeling might provide the desired model in a simple and easy to digest procedure with cheaper cost and high speed of computation. Because of its simplicity and practicality, even non-statisticians who have a very limited background in statistics could take easily the fruit and benefit of this method. In this study, unlike the standard approach that can be found in the literature, GBM process will be approached from log-normal process. This is the first result of this paper which shows the simplicity of GBM process. To identify this process, as a strong indication that a process is GBM process, we can see the value of the serial correlation. The smaller the serial correlation of log returns the higher the tendency that the process is GBM process. As the second result, for practical purposes, a new procedure of time series modeling if data are positive will be introduced. These results show that, when dealing with positive dataset, GBM time series modeling is worthwhile to be included in any introductory Time Series course especially for non-statistics students. To illustrate the practical advantages of GBM time series modeling, real case studies from industries as well as government agencies and internet will be presented and discussed.
format Article
author Djauhari, M. A.
Li, L. S.
Salleh, R. M.
author_facet Djauhari, M. A.
Li, L. S.
Salleh, R. M.
author_sort Djauhari, M. A.
title Modeling positive time series data: a neglected aspect in time series courses
title_short Modeling positive time series data: a neglected aspect in time series courses
title_full Modeling positive time series data: a neglected aspect in time series courses
title_fullStr Modeling positive time series data: a neglected aspect in time series courses
title_full_unstemmed Modeling positive time series data: a neglected aspect in time series courses
title_sort modeling positive time series data: a neglected aspect in time series courses
publisher Science Publications
publishDate 2016
url http://eprints.utm.my/id/eprint/72330/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84985906551&doi=10.3844%2fajassp.2016.860.869&partnerID=40&md5=e4abbd328007e04897bc4dc55507bb5f
_version_ 1643656410969407488
score 13.211869

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