The kernel density plot for data of each feature.

<div><p>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...

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Bibliographic Details
Main Author:	Xiangjuan Liu (618000) (author)
Other Authors:	Yunlong Li (117641) (author), Fengtong Wang (21615902) (author), Yujie Qin (12128700) (author), Zhongyu Lyu (21615905) (author)
Published:	2025
Subjects:	Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spearman correlation analysis hog price forecasting feature engineering optimization beluga whale optimization improve prediction accuracy bilstm model achieved feature dimensionality reduction optimization ” framework innovative “ decomposition average mae compared div >< p raw price series raw data modeling cumulative mae reduction stl decomposition decoupled optimization framework raw data unoptimized bilstm specific modeling higher accuracy temporal decomposition mae decrease work establishes test sets study constructs results demonstrated residual components research demonstrates integrating stl influencing factors 80 %,
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The kernel density plot for data of each feature.

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