People's Livelihood Appeal Using Big Data Analysis And Mining
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Abstract
This study presents a time series modelling and technique to partition the time series x(t) of data into long-term change trend L(t), short-term change trend S(t), and occasional change e in order to assess the vast data of people's livelihood appeal (t). Then, using this method, dissect the data of six different livelihood appeals, including unlicensed vendors, industrial noise, sewer cover, academic qualification, out-of-store operation, and public transportation. Combine the data with other data for correlation analysis, identify the underlying cause of the appeal event, and make predictions. The experimental findings demonstrate the value of time series analysis for the analysis of massive data in e-government and for mining the appeal of people's livelihoods.
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