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Causal feature selection framework for stable soft sensor modeling based on time-delayed cross mapping
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DOI：10.1016/j.aei.2026.104337.png)
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Abstract
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• Varying causal strength across time delay is considered for feature selection. • State space reconstruction is employed to deal with interdependent variables. • Time-delayed partial cross mapping is proposed for direct causal inference. • Features are selected automatically via causal strength and validation performance. • Features selected by direct causality can enhance model stability.
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