All-chain Sets Mining Algorithm for Multi-scale Nearest Time Series

Mining all-chain set in the time series is an emerging area.To the best of our knowledge,no method has been proposed to mining all-chain sets over multi-scale nearest time series.In this paper,the problem of mining all-chain sets over multi-scale nearest time series is focused.The mining problem of all-chain sets over multi-scale nearest time series is studied,and a mining algorithm with incremental computation characteristics is proposed on the basis of the existing LRSTOMP and ALLC algorithms,MTSC(mining time series all-chain sets over multi-scale nearest time series).The MTSC algorithm uses the LRSTOMP and ALLC algorithms sequentially to process the content of the 1st nearest time series member to obtain the mining results of all-chain sets over this member,while keeping the PL and PR structures associated with this member.Starting from the 2nd nearest time series member,the LRSTOMP process in the MTSC algorithm only needs to deal with the additions of the current nearest time series member with respect to the previous nearest time series member,and further combining the PL and PR on the pre-vious nearest time series member can incrementally obtain the structure of the PL and PR on the current nearest time series member,and based on which the ALLC algorithm is used to get the all-chain set mining result on that member.Compared to the Naive way using LRSTOMP and ALLC algorithms to process the content of each recent time series member,the MTSC algorithm avoids repetitive computation on all data by utilizing the idea of incremental computation,which improves the execution speed of the algorithm and has better time efficiency.Simulation experiments based on the common data samples Penguin and TiltABP verify the effectiveness of the proposed algorithm,and the experiment results show that the results of the MTSC algorithm are completely consistent with that of the Naive algorithm,and the MTSC algorithm can achieve 80%~ 88.3% improvement in time efficiency for the above data samples with an increase in space overhead of 1.1% ~ 9.7%.