Application of GA-SVM time series prediction in tax forecasting

Lu, Sheng; Cai, Zhong-jian; Zhang, Xiaobin

doi:10.1109/iccsit.2009.5234606

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Other than being used in diseases prediction, LS-SVM has also been used in taxation [10], electricity load [11], water quality [12] and many more. In [10], Genetic Algorithm-Support Vector Machine (GA-SVM) is utilized in predicting tax gross. In the developed model, GA is implemented to automatically tune the SVM parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

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Dengue Outbreak Prediction: A Least Squares Support Vector Machines Approach

Yusof¹,

Mustaffa²

2011

IJCTE

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Malaysia. Her research interest is broadly in data analysis and management for large scale computing. This includes data mining (discovering patterns of interest from data), data warehousing, information retrieval and software reuse. Zuriani Mustaffa obtained her B. Sc (Hons) in Computer Science (Software Engineering) from University Teknologi Malaysia in 2003. She just completed her M. Sc (Information Technology) from University Utara Malaysia. Her research interests are machine learning and artificial intelligence.

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Section: Literature Reviewmentioning

confidence: 99%

“…The standard framework for LS-SVM estimation is based on the primal-dual formulation [11]. Given the dataset [10]…”

Section: Least Squares Support Vector Machinesmentioning

confidence: 99%

Dengue Outbreak Prediction: A Least Squares Support Vector Machines Approach

Yusof¹,

Mustaffa²

2011

IJCTE

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An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system

Sulaiman

Mustafa

Shareef

et al. 2012

International Journal of Electrical Power & Energy Systems

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Learning Model for Analytical Prediction of Tax Revenues from Tax Invoice Information

Mendonça,

Silva,

Coutinho

2024

Revista Científica Multidisciplinar Núcleo do Conhecimento

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In Brazil, the tax on goods and services, known by the acronym ICMS, holds significant prominence in the revenue of the federative units, approximately 90%. Its value depends on economic activity, whose tax information the taxpayers record in electronic invoices issued to the tax agencies. This paper proposes a learning architecture to predict ICMS revenue through a dataset derived from tax information. The learning architecture uses a segmented approach that starts with splitting the training and validation datasets according to a given parameter. After that, the architecture fits several machine learning models for each split subset (segment). Finally, the architecture chooses the fit machine learning model (learning instance) that produces the best prediction result for each segment. These learning instances compose a hybrid instance set to predict the records of a test dataset. The proposed architecture reduced the error compared to the traditional non-segmented approaches tested (by 18.40%) and to the current methodology of the tax agency that supported this research (by 51.90%). The low prediction error suggests that the model holds promise in estimating revenue.

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Application of GA-SVM time series prediction in tax forecasting

Cited by 3 publications

References 4 publications

Dengue Outbreak Prediction: A Least Squares Support Vector Machines Approach

Dengue Outbreak Prediction: A Least Squares Support Vector Machines Approach

An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system

Learning Model for Analytical Prediction of Tax Revenues from Tax Invoice Information

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