2014The Indian Journal of Animal SciencesOpen access

Prospects of livestock and dairy production in India under time series framework

Ranjit Kumar Paul, Wasi Alam, A. K. Paul

Open full text 23 citations

Abstract

Share of livestock sector especially dairy production in total gross domestic product (GDP) has shown a continuous rise trend over the last 30 years. Autoregressive integrated moving average (ARIMA) methodology was applied for modeling and forecasting of milk production of India. Auto-correlation (AC) and partial auto-correlation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. A significant increasing linear trend in the total milk production in India was found. To this end, evaluation of forecasting is carried out with mean absolute prediction error (MAPE), relative mean absolute prediction error (RMAPE) and root mean square error (RMSE). The best identified model for the data under consideration was used for out-of-sample forecasting up to 2015.

Open-access reader

About this research paper

What this paper is about

Share of livestock sector especially dairy production in total gross domestic product (GDP) has shown a continuous rise trend over the last 30 years. Autoregressive integrated moving average (ARIMA) methodology was applied for modeling and forecasting of milk production of India. Auto-correlation (AC) and partial auto-correlation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. A significant increasing linear trend in the total milk production in India was found. To this end, evaluation of forecasting is carried out with mean absolute prediction error (MAPE), relative mean absolute prediction error (RMAPE) and root mean square error (RMSE). The best identified model for the data under consideration was used for out-of-sample forecasting up to 2015.

Why it matters

OpenAlex reports 23 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Share of livestock sector especially dairy production in total gross domestic product (GDP) has shown a continuous rise trend over the last 30 years. Autoregressive integrated moving average (ARIMA) methodology was applied for modeling and forecasting of milk production of India. Auto-correlation (AC) and partial auto-correlation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. A significant increasing linear trend in the total milk production in India was found. To this end, evaluation of forecasting is carried out with mean absolute prediction error (MAPE), relative mean absolute prediction error (RMAPE) and root mean square error (RMSE). The best identified model for the data under consideration was used for out-of-sample forecasting up to 2015.

Key concepts: Autoregressive integrated moving average, Mean absolute percentage error, Mean squared error, Production (economics), Statistics, Time series, Econometrics, Livestock

Related papers

Back to paper searchBrowse research topicsOriginal source
Prospects of livestock and dairy production in India under time series framework — Research Paper | ScholarLens