ARMA Model for Revenue Prediction

Zaw, Thura; Kyaw, Swe Swe; Oo, Aung Nway

doi:10.1145/3406601.3406617

Proceedings of the 11th International Conference on Advances in Information Technology 2020

DOI: 10.1145/3406601.3406617

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ARMA Model for Revenue Prediction

Thura Zaw

Swe Swe Kyaw

Aung Nway Oo

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2024

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Cited by 2 publications

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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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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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A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition

Wang

Zhao³

et al. 2022

Applied Sciences

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Rotating machinery is a key piece of equipment for tremendous engineering operations. Vibration analysis is a powerful tool for monitoring the condition of rotating machinery. Furthermore, vibration signals have the characteristics of time series. Hence, it is necessary to monitor the condition of vibration signal series to avoid any catastrophic failure. To this end, this paper proposes an effective condition monitoring strategy under a hybrid method framework. First, we add variational mode decomposition (VMD) to preprocess the data points listed in a time order into a subseries, namely intrinsic mode functions (IMFs). Then the framework of the hybrid prediction model, namely the autoregressive moving average (ARMA)-artificial neural network (ANN), is adopted to forecast the IMF series. Next, we select the sensitive modes that contain the prime information of the original signal and that can imply the condition of the machinery. Subsequently, we apply the support vector machine (SVM) classification model to identify the multiple condition patterns based on the multi-domain features extracted from sensitive modes. Finally, the vibration signals from the Case Western Reserve University (CWRU) laboratory are utilized to verify the effectiveness of our proposed method. The comparison results demonstrate advantages in prediction and condition monitoring.

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ARMA Model for Revenue Prediction

Cited by 2 publications

References 8 publications

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

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

A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition

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