Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Forecasting buffalo population of Pakistan using autoregressive integrated moving average (ARIMA) time series models
Jönköping University, Jönköping International Business School, JIBS, Statistics.ORCID iD: 0000-0003-0279-5305
Department of Statistics, University of Sargodha, Pakistan.
Department of Statistics, University of Sargodha, Pakistan.
Jönköping University, Jönköping International Business School, JIBS, Statistics.
Show others and affiliations
2019 (English)In: Proceedings of the Pakistan Academy of Sciences: Part A, ISSN 2518-4245, Vol. 56, no 3, p. 11-20Article in journal (Refereed) Published
Abstract [en]

Livestock plays a vital role in Pakistan’s economy. Buffalo is the primary source of milk and meat, which is a basic need for human health. So, there is a need to forecast the buffalo population of Pakistan. The main objective of the current study is to determine an appropriate empirical model for forecasting buffalo population of Pakistan to assess its future trend up to the year 2030. We apply different Autoregressive Integrated Moving Average (ARIMA) models on the buffalo population-based on fifty-years’ time-series dataset. Different model selection criteria are used to test the reliability of the ARIMA models. Based on these criteria, we perceive that ARIMA (1, 0, 0) is a more suitable model. Moreover, we also test the fitted model assumptions, such as normality and independence, to find out more accurate forecasted values. This study revealed that the buffalo population expected to increase 30% up to the year 2030 under the assumption that there is no irregular trend can be encountered during forecasted years.

Place, publisher, year, edition, pages
Giunti , 2019. Vol. 56, no 3, p. 11-20
Keywords [en]
Autoregressive Integrated Moving Average (ARIMA), Buffalo, Estimated Root Mean Square Error (ERMSE), Time Series Model
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-50689Scopus ID: 2-s2.0-85090459495Local ID: KOA;intsam;1470474OAI: oai:DiVA.org:hj-50689DiVA, id: diva2:1470474
Available from: 2020-09-24 Created: 2020-09-24 Last updated: 2021-02-26Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

ScopusFulltext

Authority records

Qasim, MuhammadOmer, Talha

Search in DiVA

By author/editor
Qasim, MuhammadOmer, Talha
By organisation
JIBS, Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 48 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf