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DCFA-iTimeNet: Dynamic cross-fusion attention network ...
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由 J Yuan 著作2025 — This component accurately quantifies variables ' historical long-term, instantaneous, and future impact, thus offering new insights into the ...
DCFA-iTimeNet: Dynamic cross-fusion attention network ...
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2024年12月10日 — Abstract and Figures. Although time series prediction research among engineering and technology has made breakthrough progress in performance, ...
DCFA-iTimeNet: Dynamic cross-fusion attention network for ...
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The issue of co-occurrence OPP mining (COP) is addressed and an algorithm named COP-Miner is proposed to discover COPs from historical time series that ...
DCFA-iTimeNet: Dynamic cross-fusion attention network for ...
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DCFA-iTimeNet: Dynamic cross-fusion attention network for interpretable time series prediction ... When consumers need more interpretability of artificial ...
DCFA-iTimeNet: Dynamic cross-fusion attention network for ...
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DCFA-iTimeNet: Dynamic cross-fusion attention network for interpretable time series prediction. DCFA-iTimeNet:用于可解释时间序列预测的动态交叉融合注意力网络.
Volume 55, Issue 2 | Applied Intelligence
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... attention dynamic graph neural network. Lipeng Qian; Qiong ... DCFA-iTimeNet: Dynamic cross-fusion attention network for interpretable time series prediction.
Applied Intelligence
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In this paper, we present a framework for assessing geopolitical news based on local sentiment and public attention. Our approach uses data from social media ...
Comparison of predicted and true values on the proposed ...
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DCFA-iTimeNet: Dynamic cross-fusion attention network for interpretable time series prediction ... Although time series prediction research among ...
KLU-APIN: Vol 55, No 2 - ACM Digital Library
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Abstract. In this paper, we present a framework for assessing geopolitical news based on local sentiment and public attention. Our approach uses data from ...
An interpretable automated feature engineering framework ...
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由 M Liu 著作2024被引用 5 次 — This framework automatically transforms the nonlinear relationships between numerical features and labels into linear relationships, conducts feature cross ...