Robustness Verification of Temporal Data Classification Model Based on Big Data
Irreversible timing data is an important part of the Internet of Things industry.How-ever,it is difficult to identify and classify the time series data from complex and diverse sources to ensure the smooth operation of the calculation model.To solve this problem,a time-series data classification model based on big data is proposed.The local projection algorithm is used to en-hance and identify the key features of the time-series data obtained.The dynamic principal com-ponent analysis method and the sparrow search algorithm are used to construct the data classifica-tion model.The results show that the FPR value of the model is 14.38％higher than that of other algorithms on average.Under various noise interference,the FPR value of the model de-creases less than that of other algorithms,and its robustness is obviously better than that of other algorithms.
Big dataTime series dataLocal projection methodDynamic principal component analysisSparrow search algorithm
NETL
NSTL
万方数据
