Temporal Trends and Future Projections: A Deep Dive into India’s Buffalo Milk Production Through Time Series Modelling

Vaisakh Venu, P.K. Anjitha, P.R. Vipin, E.R. Ramdas, R. Senthilkumar, B. Sreenath
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Abstract

Background: Buffalo milk production in India plays a significant role in the global dairy market, with a rich history deeply intertwined with the country’s economy and culture. Over six decades, the dynamics of buffalo farming have been pivotal in shaping India’s dairy landscape. Methods: This paper delves into the subject by analysing a comprehensive time series dataset spanning six decades. The focus lies on understanding the economic and cultural significance of buffalo farming, particularly in relation to milk production. Four forecasting models-ARIMA, SES, Seasonal Naive and ETS-are employed to discern temporal patterns in buffalo milk production. Result: The study reveals that the ARIMA and ETS models outperform SES and Seasonal Naive models in capturing and elucidating data behaviour. Their superior performance underscores their efficacy in predicting buffalo milk production trends accurately. These findings offer valuable insights for policymakers and stakeholders aiming to optimize buffalo milk production and foster long-term growth in India’s dairy sector.
时间趋势和未来预测:通过时间序列模型深入研究印度的水牛奶产量
背景:印度的水牛奶生产在全球乳制品市场上发挥着重要作用,其丰富的历史与该国的经济和文化深深地交织在一起。六十多年来,水牛养殖业的发展对印度乳业格局的形成起到了举足轻重的作用。方法:本文通过分析跨越六十年的综合时间序列数据集来深入探讨这一主题。重点在于了解水牛养殖的经济和文化意义,尤其是与牛奶生产相关的意义。本文采用了四种预测模型--ARIMA、SES、Seasonal Naive 和 ETS--来分析水牛奶产量的时间模式。结果:研究表明,在捕捉和阐明数据行为方面,ARIMA 和 ETS 模型优于 SES 和 Seasonal Naive 模型。它们的卓越表现突出表明了它们在准确预测水牛奶生产趋势方面的功效。这些发现为旨在优化水牛奶生产和促进印度乳业长期增长的政策制定者和利益相关者提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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