摘要
不可逆的时序数据是物联网产业的重要组成部分。但如何识别来源复杂且多样的时序数据,并对其进行分类,保障计算模型的顺畅运转是其中的难点。针对此问题,研究提出了基于大数据的时序数据分类模型,采用局部投影算法增强并识别获取的时序数据关键特征,使用动态主成分分析方法,联合麻雀搜索算法构建数据分类模型。研究结果表明:模型的FPR值平均比其他算法高出14.38%,在多种噪声干扰下,模型的FPR值下降幅度较其他算法更小,鲁棒性明显优于其他算法。
Irreversible timing data is an important part of the Internet of Things industry.However,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 enhance and identify the key features of the time-series data obtained.The dynamic principal component analysis method and the sparrow search algorithm are used to construct the data classification 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 decreases less than that of other algorithms,and its robustness is obviously better than that of other algorithms.
作者
李艳
LI Yan(Shuozhou Normal College,Shuozhou Shanxi 036000,China)
出处
《长江信息通信》
2024年第11期70-72,共3页
Changjiang Information & Communications
关键词
大数据
时间序列数据
局部投影法
动态主成分分析
麻雀搜索算法
Big data
Time series data
Local projection method
Dynamic principal component analysis
Sparrow search algorithm
