Tariffs Forecasting Based on Wavelet ARIMA Model
Article No.: 146, Pages 1 - 6
Abstract
It is important to adopt a scientific and effective method to forecast tariffs. Using the tariff data of China from January 2014 to July 2019, the tariffs were predicted and analyzed based on the wavelet ARIMA model. Firstly, multi-scale wavelet decomposition of tariff time series is carried out, and the appropriate decomposition scale is determined and reconstructed to obtain the filtered denoising sequence. Further, the appropriate ARIMA model is established for the denoising sequence and predicted. The results show that the wavelet ARIMA model has smaller prediction error and higher precision than the conventional ARIMA model, and has good application value in the prediction of time series data.
References
[1]
Kou Tiejun,Jin Shuanghua.Research on the Application of Grey System Theory in Tax Forecasting[J].Research in Quantitative and Technical Economics, 2001(12)
[2]
Liu Yan.Using the neural network model to predict the national tax revenue of Jilin Province[J].Taxation and Economy,2014(02):110-112.
[3]
Chang Qing,Liu Qiang.Research on tax forecast model based on support vector machine[J].Computer Engineering and Design,2007(07)
[4]
Wang Wenchen, Tang Xiufang. The Application of Principal Component Analysis Method in my country's Tax Forecasting[J]. Journal of Xinyang Normal University (Natural Science Edition), 2006(04)
[5]
Lai Huihui. Prediction of VAT output tax based on ARMA model in the context of big data[J]. Taxation Research, 2019(02)
[6]
Ma Ming, Yang Xu, Wang Peng. Winter outpatient volume analysis and prediction based on wavelet analysis and ARMA model[J]. Chinese Health Statistics, 2018, 35(04)
[7]
Shi Hongyan, You Zuojun, Chen Zhongju. Analysis and prediction of Shanghai stock index based on ARIMA model of wavelet analysis[J]. Mathematics in Practice and Knowledge, 2014, 44(23)
[8]
Hou Puguang, Qiao Zequn. Research on housing price prediction based on wavelet analysis and ARMA model[J]. Statistics and Decision, 2014(15)
[9]
Wang Jingjing, Gao Shan, Cui Yuwei, Zhou Pinggen. Forecast of VAT output tax based on wavelet ARMA model[J]. Taxation Research, 2019(10)
[10]
Zhou Wei. Application of Wavelet Analysis Based on MATLAB [M]. Xi'an: Xidian University Press, 2010
[11]
Chen Qiang. Advanced Econometrics and State Application (Second Edition [M]. Beijing: Higher Education Press, 2014
[12]
Zhou Guangsu State statistical analysis and application [M]. Beijing: Machinery Industry Press, 2011
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- Tariffs Forecasting Based on Wavelet ARIMA Model
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Published In
January 2021
1142 pages
ISBN:9781450389570
DOI:10.1145/3448734
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Association for Computing Machinery
New York, NY, United States
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Published: 17 May 2021
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CONF-CDS 2021
CONF-CDS 2021: The 2nd International Conference on Computing and Data Science
January 28 - 30, 2021
CA, Stanford, USA
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