LSTM-MSTCN-XGBoost混合模型的时空数据特征挖掘

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中图分类号：TN911.7-34；TP391 文献标识码：A 文章编号：1004-373X（2025）16-0157-04

Spatiotemporal data feature mining of LSTM-MSTCN-XGBoost hybrid model

LIYangzheng'，YI Jiliang²

inanUniversityofTechnology，Zhuzhou412Ooo，China;2.GuilinUniversityofAerospaceTechnology，Guilin541004，Ch

Abstract：Duetothespatiotemporalcorelationanddynamicevolutionofspatiotemporaldata，featureminingisdificult. Thesingledimensionalanalysismethodsaredificulttocomprehensivelycaptureitslong-termandshort-termvariation characteristicsofspatiotemporalchanges，whichcaneasilyleadtothelossofkeyinformation.herefore，aspatiotemporaldata feature mining methodbasedontheLSTM-MSTCN-XGBoost hybrid modelis proposed.TheOWLisusedtoconductthe formal modelingofspatiotemporaldata，LSTMandMSTCNmodelsareused tominelong-termandshort-termfeaturesrespectively，and theXGBoostmodel isinputtofuseandoutputfeaturepaternrecognitionresults.Theexperimentalresultsshowthat the spatiotemporaldata features extractedbytheproposedmethod haveaglobal spatiotemporal Moran'sIindex exceeding O.9.In traficspatiotemporaldatamining，thecharacterizationofcongestionfeaturesisalsomorerealistic，providinganeffective approach for spatiotemporal data mining and intelligent decision-making.

Keywords：spatiotemporaldata; featuremining;LSTMmodel;MSTCNmodel; XGBoostmodel;OWLformalmodeling

0 引言

时空数据指包含时间特征和空间特征的数据，此类数据具有时空关联性与动态变化性。（剩余4474字）