一种时间序列频繁模式挖掘算法及其在WSAN行为预测中的应用

Time Series Frequent Pattern Mining Algorithm and its Application to WSAN Behavior Prediction

该文提出FPM(Frequent Pattern Mining)算法充分考虑频繁模式在时间序列中出现次数和分布。基于这些不同分布的频繁模式扩展MAMC(Mixed memory Aggregation Markov Chain)模型提出FMAMC(Frequent pattern based Mixed memory Aggregation Markov Chain)模型。将FPM和FMAMC应用到实际的智能楼宇项目中,证明和现有算法相比FPM算法具有较好的时间性能,FMAMC模型能够比MAMC模型更准确的预测WSAN节点行为。

A frequent pattern mining algorithm FPM （Frequent Pattern Mining） is proposed.FPM not only considered the frequency but also the distribution of the frequent pattern along the time series.Based on these different types of frequent patterns,MAMC （Mixed memory Aggregation Markov Chan） is extended to FMAMC （Frequent pattern based Mixed memory Aggregation Markov Chan） model.The proposed algorithm and model are applied to a smart building project,experiment and practice both demonstrate FPM is efficient than existing algorithms and FMAMC model could more accurately predict the node behavior in WSAN than MAMC.