From the perspective of agribusiness, the market price of live pigs reflects the current demand. Therefore, tracking and forecasting market prices are important tasks in agrimanagement, by which the production schedule can be adjusted to increase profit. An agricultural internet platform was developed as an integrated cloud service for market tracking. To quantitatively forecast online pig trading, in this study, a short‐term forecasting model of the pig price index was developed; the model automatically retrieved historical data as a training data set and determined the price index forecast with an autoregressive integrated moving average (ARIMA) algorithm for a time‐series analysis. The mean square error (MSE) of the AR(1) model for predicting the pig price index in Henan Province was 159.010, and the MSE of the ARIMA(1,1) model for predicting pig price index in Fujian Province was 92.294. The results demonstrated that the error between the predicted calculation and verification test results was small, and the results efficiently improved the prediction accuracy (EconLit citations: C6, L86, Q1).

中文翻译：

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农业互联网平台上生猪价格指数的短期预测

从农业综合企业的角度看，生猪的市场价格反映了当前的需求。因此，跟踪和预测市场价格是农业管理中的重要任务，通过它可以调整生产进度以增加利润。农业互联网平台被开发为用于市场跟踪的集成云服务。为了定量预测生猪在线交易，本研究建立了生猪价格指数的短期预测模型。该模型自动检索历史数据作为训练数据集，并使用自回归综合移动平均（ARIMA）算法确定价格指数预测，以进行时间序列分析。用于预测河南省生猪价格指数的AR（1）模型的均方误差（MSE）为159.010，ARIMA（1）的MSE为 1）福建省生猪价格指数预测模型为92.294。结果表明，预测的计算结果与验证测试结果之间的误差很小，并且结果有效地提高了预测准确性（EconLit引文：C6，L86，Q1）。