Main Article Content
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.
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.