Analysis of raw data prediction results.

This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). The research demonstrates that the synergy of temporal decomposition, feature dimensionality reduction, and intelligent optimization reduces hog price prediction errors by over 80%, with STL-PCA feature engineering contributing 67.4% of the improvement. This work establishes an innovative “decomposition-reconstruction-optimization” framework for agricultural economic time series forecasting.

本研究以生猪价格时间序列数据及其影响因素为基础，构建多阶段混合预测模型以提升预测精度。首先，针对原始生猪价格序列，采用Prophet、差分自回归移动平均模型（ARIMA）、长短期记忆网络（LSTM）等七种基准模型开展预测实验，结果显示深度学习模型的性能显著优于传统统计方法。随后，通过STL分解（Season-Trend Decomposition using Loess）将原始序列拆解为趋势、季节与残差分量并分别建模，相较于直接对原始数据建模，平均绝对误差（Mean Absolute Error, MAE）的均值降低了22.6%。进一步结合斯皮尔曼相关性分析与主成分分析（PCA）构建多维特征集，实现了预测精度的大幅提升：双向长短期记忆网络（BiLSTM）模型的累计MAE降幅达83.6%，从原始数据建模时的1.65降至STL-PCA框架下的0.27；而Prophet这类传统模型在经过特征工程优化后，MAE降幅也达到了82.2%。最后，经白鲸优化算法（Beluga Whale Optimization, BWO）调优的STL-PCA-BWO-BiLSTM混合模型在测试集上取得了最优性能（均方根误差Root Mean Squared Error, RMSE=0.22，平均绝对误差MAE=0.16，平均绝对百分比误差Mean Absolute Percentage Error, MAPE=0.99%），其预测精度较未优化的BiLSTM模型（MAE=0.27）提升了40.7%。本研究证实，时间序列分解、特征降维与智能优化三者的协同作用可将生猪价格预测误差降低80%以上，其中STL-PCA特征工程贡献了67.4%的精度提升。本研究为农业经济时间序列预测构建了创新性的"分解-重构-优化"框架。