Analysis of STL-PCA 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.

本研究基于生猪价格时序数据及其影响因素，构建多阶段混合预测模型以提升预测精度。首先，将Prophet、自回归积分滑动模型（Autoregressive Integrated Moving Average, ARIMA）、长短期记忆网络（Long Short-Term Memory, LSTM）等7种基准模型应用于原始生猪价格序列，实验结果表明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（Seasonal and Trend decomposition using Loess, STL）将原始序列拆分为趋势项、季节性项与残差项并分别进行建模，相较原始数据直接建模，平均绝对误差（Mean Absolute Error, MAE）降低了22.6%。进一步结合斯皮尔曼相关分析与主成分分析（Principal Component Analysis, PCA）降维构建多维特征集，实现了预测精度的大幅提升：双向长短期记忆网络（Bidirectional Long Short-Term Memory, BiLSTM）模型的MAE累计降幅达83.6%，从原始数据场景下的1.65降至STL-PCA框架下的0.27；而Prophet等传统模型在经过特征工程优化后，MAE降幅也达到82.2%。最终，经白鲸优化算法（Beluga Whale Optimization, BWO）调参的STL-PCA-BWO-BiLSTM混合模型在测试集上取得最优性能（均方根误差（Root Mean Square Error, RMSE）=0.22，MAE=0.16，平均绝对百分比误差（Mean Absolute Percentage Error, MAPE）=0.99%），相较于未优化的BiLSTM模型（MAE=0.27），预测精度提升40.7%。本研究证实，时序分解、特征降维与智能优化的协同作用可使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序预测领域构建了创新性的“分解-重构-优化”框架。