泰米尔纳德邦西部地区牛奶生产的建模与预测

S. Shankar, R. Ajaykumar, S. Ananthakrishnan, A. Aravinthkumar, K. Harishankar, T. Sakthiselvi, C. Navinkumar
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引用次数: 0

摘要

背景:在印度,乳制品行业正在急剧扩张。泰米尔纳德邦牛奶合作社为该邦乳制品行业的发展做出了重大贡献。在为奶农提供经济收入和满足客户需求方面,牛奶生产的鉴定是印度主要的金融业务之一。考虑到这一点,了解未来的产量对于促进和维持该行业的增长和发展至关重要。方法:本研究试图利用时间序列模型预测泰米尔纳德邦的牛奶产量。从1976年到2020年每年的牛奶数据。该研究采用自回归综合移动平均(ARIMA)和人工神经网络(ANN)选择合适的随机模型来预测泰米尔纳德邦的牛奶产量。进一步用于牛奶生产的统计建模程序表明,选择合适的时间序列模型将始终取决于数据的性质。结果:结果显示,尽管ARIMA模型被认为是最强大的模型,但它仍然被选为最佳模型。2020-2025年预测牛奶产量的复合年增长率为0.02%。模型充分性标准如RMSE, MAPE和MAE被使用。基于观测结果,选择ARIMA模型(1,1,2)作为人工神经网络模型的最佳模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling and Forecasting of Milk Production in the Western Zone of Tamil Nadu
Background: In India, the dairy business is expanding dramatically. Tamil Nadu milk cooperatives significantly contribute to the growth of the dairy sector in the state. In terms of delivering economic income for dairy smallholders and satisfying customer demand, the identification of milk production is one of the primary financial operations made in India. Considering this, it is crucial to understand future production to enhance and sustain the sector’s growth and development. Methods: The present investigation attempts to predict and forecast milk production in Tamil Nadu using time series models. Yearly milk data from 1976 to 2020 was taken. The study considered Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to select the appropriate stochastic model for forecasting milk production in Tamil Nadu. Further statistical modeling procedures employed for milk production reveal that the selection of a suitable time series model will always depend on the nature of the data. Result: Results revealed that the ARIMA model is selected as the best model despite ANN, even if it is considered the most powerful model. The CAGR for forecasted milk production from 2020-2025 was 0.02%. Model adequacy criteria like RMSE, MAPE and MAE are used. Based on observation ARIMA model (1, 1, 2) is chosen as the best model over the ANN model.
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