基于區(qū)域結(jié)構(gòu)探測與邊緣辨識(shí)發(fā)現(xiàn)時(shí)間序列因果關(guān)系轉(zhuǎn)換

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中圖分類號(hào)：TP391 文獻(xiàn)標(biāo)志碼：A

Discovering Causal Transitions in Time Series Based on Region Structure Detection and Edge Identification

XIE Jiea， WANG Kaijuna，b， FANG Yinga，b， LUO Tianjiana， b （a.Collegeof Computer and Cyber Security，b.Digit Fujian Internet-Things Laboratory ofEnvironmental Monitoring， FujianNormal University，F(xiàn)uzhou 35O117，F(xiàn)ujian，China）

Abstract：Toimprove theaccuracyof existing methods for mining causal relationships over time，forabinarytime series containing one causal region，amethod was proposed tominethecausal transition pointsof timeseries byidentifying differentregional structuresandtheedgesof structure.Themethod designedthe posiblepositions ofthe causal region in timeseries as left，right，and center structure，used the existing causalitydiscovery methods to detect therough causal region，anddistinguisheditasacertainregional structureaccording tothediferentcharacteristicsof theregionstructures.According to the characteristics of diffrentregional structures，coresponding edge identificationmeasures were designed，and gradualy increasing detection windows andcausality intensity indexes were setto identify regional structural edges as causal transition points，and improvethe identification accuracyofcausal transition points.Experimentson two simulated datasetsand tworeal datasetsverified theaccuracyof the proposed method inrecognizing causal transition points.The resultsshow thatthe average acuracyofcausal transition pointsobtained bythe proposed method using Grangercausality scores onseparable simulated datasets is higher than those of the comparison methods，the average auracyof causal transition points obtained by convergent cross mapping causality scores on weakly coupled simulated datasets is higher than those of thecomparison methodatcoupling degresof O.O1and O.5O，and the accuracyof causal transitionpoints obtained byusing Grangercausalityscoresontworeal datasetsis higher than thatof thecomparison method.

Keywords：timeseries；causalityelation；causalrelation transition；convergentcross maping；Grangercausalitydetection

挖掘時(shí)間序列的因果關(guān)系，理解變量之間的因果關(guān)系對(duì)于預(yù)測、制定決策和解決問題至關(guān)重要[①]分析時(shí)間序列因果關(guān)系的常用方法包括Granger因果檢驗(yàn)[2-3]和收斂交叉映射因果檢測（CCM）[4]Granger因果檢驗(yàn)和CCM方法在探索時(shí)間序列的因果關(guān)系方面的側(cè)重點(diǎn)不同：對(duì)于可分離系統(tǒng)和緊密度很高的耦合系統(tǒng)，Granger因果檢驗(yàn)方法有效；對(duì)于緊密度較低的弱耦合系統(tǒng)，CCM方法有效。（剩余10383字）