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ARMA Model for Revenue Prediction

Published: 03 July 2020 Publication History

Abstract

For every country in over the world, tax revenues appear to be the main engines contributing to the growth momentum. The prediction of tax revenues is one of the main challenges of the Myanmar Internal Revenue Department. It is not easy to get an accurate prediction of the tax revenues of the coming financial year. This is an important issue because the obtained results are used in the decision making of the target budget for the coming year's revenues. In this paper, a model of time series analysis based on Autoregressive Moving-Average (ARMA) for the forecast of the tax revenue collection is introduced. The results were more accurate in comparison to the outcome of the IRD that is estimated with the traditional estimation method. ARMA models are constructed for the prediction of each of four different tax revenues, income tax, commercial tax, lottery tax, and stamp duties. Although the proposed method is applied to four main types of tax revenues, only forecasting of commercial tax revenues is found out in the experiments of this paper. The results show that the error rate reduces to one-third of the traditional forecasting method of the Internal Revenue Department.

References

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Cited By

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  • (2024)Revenue Prediction using Sequential Machine Learning2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC61858.2024.10714745(1492-1496)Online publication date: 3-Oct-2024
  • (2022)A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing ConditionApplied Sciences10.3390/app1208385412:8(3854)Online publication date: 11-Apr-2022

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cover image ACM Other conferences
IAIT '20: Proceedings of the 11th International Conference on Advances in Information Technology
July 2020
370 pages
ISBN:9781450377591
DOI:10.1145/3406601
© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

In-Cooperation

  • Microsoft Corporation: Microsoft Corporation
  • NECTEC: National Electronics and Computer Technology Center
  • KMUTT: King Mongkut's University of Technology Thonburi

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2020

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Author Tags

  1. Auto-Regressive Moving Average (ARMA) Model
  2. Error Rate
  3. Forecasting
  4. Time Series Analysis
  5. tax revenues

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IAIT2020

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Overall Acceptance Rate 20 of 47 submissions, 43%

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View all
  • (2024)Revenue Prediction using Sequential Machine Learning2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC61858.2024.10714745(1492-1496)Online publication date: 3-Oct-2024
  • (2022)A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing ConditionApplied Sciences10.3390/app1208385412:8(3854)Online publication date: 11-Apr-2022

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