The big data analysis and mining of people's livelihood appeal based on time series modeling and algorithm

The big data analysis and mining of people's livelihood appeal based on time series modeling and algorithm

연구 분야: Databases

학회: 2020 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)

In order to analyze the big data of people's livelihood appeal, this paper proposes a time series modeling and algorithm to decompose the time series {x(t)} of data into long-term change trend L(t), short-term change trend S(t) and occasional change e(t). Then use this method to break down the data of six types of people's livelihood appeal such as unlicensed vendor, industrial noise, sewer cover, academic qualification, out-of-store operation and public transportation, combine other data for correlation analysis, find out the cause of the appeal event and make predictions. The experimental results verify the effectiveness of time series analysis in big data analysis and mining of people's livelihood appeal, and it is an useful attempt in the analysis of e-government big data.