Tax service volume forecasting based on informer

Shengbo Wang; Chutong Deng; Jiale Chen; Ning Xiao; Shaobo Chen; Yujuan Quan

doi:10.1117/12.2640994

2 December 2022 Tax service volume forecasting based on informer

Shengbo Wang, Chutong Deng, Jiale Chen, Ning Xiao, Shaobo Chen, Yujuan Quan

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 1228806 (2022) https://doi.org/10.1117/12.2640994
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

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.

Citation Download Citation

Shengbo Wang, Chutong Deng, Jiale Chen, Ning Xiao, Shaobo Chen, and Yujuan Quan "Tax service volume forecasting based on informer", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 1228806 (2 December 2022); https://doi.org/10.1117/12.2640994

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