Intelligent resource planning is expected to be of significant help in promoting the efficiency in tax halls, while service volume forecasting is a basis for further research on intelligent resource allocation and optimization. This paper uses the historical data from a tax hall in one of the most developed regions in China as dataset and studies the tax service volume forecasting based on time series forecasting, including five different models based on traditional and deep learning methods: ARIMA, Prophet, DeepAR, Transformer and the newly-proposed Informer. Aiming at selecting the best model for forecasting, the performance of the models on different business scenarios is compared. The experiment results indicate that the Informer has shown good performance and required reduced complexity in univariate forecasting. The results are expected to provide reference and further promote the research on intelligent task scheduling and resource allocation to enhance the service quality in the tax hall.
Cloud computing, as a current research hotspot in the field of information, is a new storage method combining distributed database technology and web server technology, which achieves specific goals mainly through the integration of a large number of resources in a virtual environment. In this paper, we explore the application of cloud computing to computer data processing and design a data processing system. User requests are first returned to the corresponding server via a network connection, and then algorithms are used to derive the relevant information, which is fed back to the next interactive interface for users to browse, query and compare. The system demonstrates that cloud computing technology can ensure effective data processing after initialisation and can improve the response time to user requests in some way.
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