Error correction method based on data transformational GM(1,1) and application on tax forecasting

Zhang, Yu; Yang, Cheng‐Hong; Zhang, Zheng; Jiao, Jian

doi:10.1016/j.asoc.2015.09.001

Cited by 37 publications

(10 citation statements)

References 20 publications

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“…Under normal circumstances, these data can be obtained in equal time intervals. Time series analysis has three basic functions: first, use distance metrics to determine the similarity of different time series; second, examine the structure of time series graphs to determine the behavior of time series; third, use historical time series graphs to predict future values of data [17]. Classification is used to predict the category of data objects, that is, use existing information to predict unknown events.…”

Section: Simulation Experiments Designmentioning

confidence: 99%

Analysis and Application of Grey-Markov Chain Model in Tax Forecasting

Zhang

Chen

2021

Journal of Mathematics

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Tax data is a typical time series data, which is subject to the interaction and influence of economic and political factors and has dynamic and highly nonlinear characteristics. The key to correct tax forecasting is the choice of forecasting algorithm. Traditional tax forecasting methods, such as factor scoring method, factor regression method, and system adjustment method, have a certain guiding role in actual work, but there are still many shortcomings, such as the limitation from the distribution and size of sample data and difficulty of grasping the nonlinear phenomena in economic system. Grey-Markov chain model formed by the combination of grey forecasting and Markov chain forecasting can not only reveal the general developmental trend of time series data, but also predict their state change patterns. Based on the summary and analysis of previous research works, this paper expounds the current research status and significance of tax forecasting, elaborates the development background, current status, and future challenges of the Grey-Markov chain model, introduces the basic principles of grey forecasting model and Markov chain model, constructs the Grey-Markov chain model, analyzes the model’s residual error and posteriori error tests, conducts the analysis of Grey-Markov chain model, performs grey forecasting model construction and its state division, implements the calculation of transition probability matrix and the determination of tax forecasting value, discusses the application of the Grey-Markov chain model in tax forecasting, and finally carries out a simulation experiment and its result analysis. The study results show that, compared with separate grey forecasting, Markov chain forecasting, and other commonly used time series forecasting methods, the Grey-Markov chain model increases the accuracy of tax forecasts by an average of 2.3–13.1%. This indicates that the combinative forecasting of Grey-Markov chain model can make full use of the information provided by time series data for tax analysis and forecasting. It can not only avoid the influence of economic, political, and human subjective factors, but also have simple calculations, higher accuracy, and stronger practicality. The study results of this paper provide a reference for further researches on the analysis and application of Grey-Markov chain model in tax forecasting.

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Section: Simulation Experiments Designmentioning

confidence: 99%

Analysis and Application of Grey-Markov Chain Model in Tax Forecasting

Zhang

Chen

2021

Journal of Mathematics

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“…No Publication Modelling 36 [1], [2], [3], [5], [6], [7], [9], [12] , [13], [14], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [32], [33], [38], [39], [42], [43], [47], [49],[57], [58], [61], [62], [63] Forecasting 32 [3], [4], [5], [8], [10], [11], [15], [16], [17], [29], [30], [34], [35], [36], [37], [40],…”

Section: Parametersmentioning

confidence: 99%

“…TThe challenges of heteroscedasticity persistence which is not clearly defined either from the data or the methodological approach (technique) used for the analysis, either from the angle tax revenue and stock market index data., Meanwhile, research on heteroscedasticity of the financial time series modelling and forecasting has drawn a lot of attention by the researchers in recent times. Tax revenue heteroscedasticity consists of revenue variability during the process of the business cycle [1,2]. It has become an area of interest for fiscal administrators and policymakers working inside the framework of secured budget desires [3,4].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Heteroscedasticity Modelling and Forecasting of Tax Revenue in a Developing Economy: A Review

Alhassan¹,

Yusof²,

Norrulashikin³

et al. 2020

IJMH

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Tax revenue modelling and forecasting is very crucial for revenue collection and tax administration management. The dynamics of heteroscedasticity in the financial time series (tax revenue) in the domain of technique used to model and predict tax revenue in the emerging economy threw us to this investigation. The reviews are categorized into two the tax revenue and stock exchange index. Five factors were considered in this studies modelling, forecasting, linear model, nonlinear model and heteroscedasticity, it is on this note that we syntheses over 75 studies from the literature to consider the pattern of reporting tax revenue and stock market index. Thus, from the reviewed literature, we inferred that the pattern of reporting tax revenue data and the analytical techniques employed by most of these studies are responsible for the instability (volatility) in the financial time series forecasting. Also, results revealed that linear models are mostly applied to tax revenue data with fewer non-linear models, while combination and single non-linear models were mostly used for stock exchange data. Thus, we recommend the combination of linear and nonlinear models for both tax revenue and stock exchange data which can minimize the error of heteroscedasticity in the forecasting of tax revenue in a developing economy.

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“…Through real data from Hebei Province, China, the developed combined model is proved to be effective compared with single models, especially in multistep forecasting. Yu et al [37] realized that it is difficult to predict taxes precisely based on a single model. Thus, they developed a new technique by applying error correction to predict the subsequent error and go back to correct the previous error.…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

2019

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Precise wind speed prediction is increasingly practical for sustained and stable wind energy utilization considering the growing portion of wind energy in the global electric grid. Although plenty of wind speed forecasting approaches have been devoted to improving forecasting performance, the majority have neglected the utilization of error information, integrated the forecasting value of every component with simple ensemble approaches, and ignored forecasting stability, which may make the forecasting results poor. Considering the above drawbacks, a two-stage forecasting system is performed in our study based on the data preprocessing approach, improved multi-objective optimization algorithm, error correction and nonlinear ensemble strategy. The developed system effectively overcomes the shortcomings of previous models and observably prompts wind speed forecasting capacity. For investigating the prediction capacity of the developed system, six wind speed series obtained from China and Spain are applied as case sites. The forecasting consequences indicate that the developed system is more conducive to enhancing forecasting precision and stability than other involved models, which can provide beneficial assistance for wind speed forecasting. INDEX TERMS Wind speed forecasting, multi-objective optimization algorithm, ensemble strategy, forecasting accuracy.

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Error correction method based on data transformational GM(1,1) and application on tax forecasting

Cited by 37 publications

References 20 publications

Analysis and Application of Grey-Markov Chain Model in Tax Forecasting

Analysis and Application of Grey-Markov Chain Model in Tax Forecasting

Dynamics of Heteroscedasticity Modelling and Forecasting of Tax Revenue in a Developing Economy: A Review

Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

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