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1.	Qasim, Muhammad, et al. (author) Forecasting buffalo population of Pakistan using autoregressive integrated moving average (ARIMA) time series models 2019 In: Proceedings of the Pakistan Academy of Sciences: Part A. - : Giunti. - 2518-4245 .- 2518-4253. ; 56:3, s. 11-20 Journal article (peer-reviewed)abstract 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.

Qasim, Muhammad, et al. (author)
Forecasting buffalo population of Pakistan using autoregressive integrated moving average (ARIMA) time series models
2019
In: Proceedings of the Pakistan Academy of Sciences: Part A. - : Giunti. - 2518-4245 .- 2518-4253. ; 56:3, s. 11-20
Journal article (peer-reviewed)abstract
- 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.

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