基于奇异谱分解和LSTMARIMA组合模型的生猪价格预测

doi:10.13733/j.jcam.issn.2095-5553.2024.05.027

摘要/Abstract

摘要： 针对生猪价格波动过于剧烈难以预测的问题，提出基于奇异谱分解的LSTMARIMA组合模型对生猪价格进行预测。以2000年1月—2021年12月的月度价格数据作为样本，利用奇异谱分析对生猪价格数据进行分解，得到趋势项和波动项，选用累计贡献率达前70%的构建趋势项，剩下的30%构造波动项。趋势项非平稳且具有长记忆性，对其建立LSTM模型；波动项平稳，对其建立ARIMA模型，最后将两部分预测结果重组作为生猪价格的预测值，构建LSTMARIMA组合预测模型。将预测值和生猪真实价格进行对比，结果表明：预测值与真实值之间的均方根误差RMSE为2.75，平均绝对百分比误差MAPE为10.81%，平均绝对误差MAE为2.27，方向对称性DS为81.81；此组合模型能很好地预测生猪价格走势，对我国生猪价格预测具有更高地适用性与参考。

关键词: 生猪价格预测, 奇异谱分析, 组合模型, LSTM, ARIMA

Abstract: Aiming at the problem that the fluctuation of pig price is too violent and difficult to predict, a LSTMARIMA combination model based on singular spectrum decomposition is proposed to predict pig price. Taking the monthly price data from January 2000 to December 2021 as a sample, the pig price data is decomposed by singular spectrum analysis to obtain the trend term and fluctuation term. The trend term with the cumulative contribution rate of the first 70% is selected to construct the trend term, and the remaining 30% is used to construct the fluctuation term. The trend item is nonstationary and has long memory, and the LSTM model is established. The fluctuation term is stable, and the ARIMA model is established. Finally, the prediction results of the two parts are recombined as the prediction value of pig price, and the LSTMARIMA combined prediction model is constructed. The results show that the RMSE between the predicted value and the real value is 2.75, MAPE is 10.81%, MAE is 2.27 and DS is 81.81. This combined model can well predict the price trend of newborn pigs, and has higher applicability and reference value for the prediction of pig prices in China.

Key words: forecast of pig price, singular spectrum analysis, combination model, LSTM, ARIMA

中图分类号:

F323.7

付莲莲, 方青, 袁冬宇, 滕佳敏. 基于奇异谱分解和LSTMARIMA组合模型的生猪价格预测[J]. 中国农机化学报, 2024, 45(5): 176-181.

Fu Lianlian, Fang Qing, Yuan Dongyu, Teng Jiamin. Forecasting of pig price fluctuation based on SSA and LSTMARIMA combination model[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(5): 176-181.

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