The comparison results between the multi-feature trained LSTM and the seven univariate forecasting models for the indicator of import of coal.

The comparison results between the multi-feature trained LSTM and the seven univariate forecasting models for the indicator of import of coal.

<p>The results show that the multi-feature trained LSTM (i.e., LSTM_Multi) outperforms the seven univariate forecasting models.</p>

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Bibliographic Details
Main Author:	Jiao He (2926455) (author)
Other Authors:	Yuhang Li (141475) (author), Xiaochuan Xu (1700947) (author), Di Wu (23906) (author)
Published:	2025
Subjects:	Ecology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified real 880 pieces help governments optimize ensembling ), making conducting extensive experiments built five models adaptive weighting strategy ensure energy security adjust energy strategies 39 factors regarding different learning structures four targeted indicators forecast targeted indicators e ., import coal ), respectively energy consumption forecasting achieve accurate forecasting targeted indicators energy consumption different indicators consumption forecasting uncertain factors different correlations xlink "> various features three parts temporal constraints optimal features model integration model building many complex feature engineering extremely challenging comprehensively capture
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