The use of fuzzy set theory for forecasting corporate tax revenues

  • Authors:

  • Eliahu Shnaider;Abraham Kandel

  • Affiliations:

  • Department of Computer Science and The Institute for Expert Systems and Robotics, The Florida State University, Tallahassee, FL;Department of Computer Science and The Institute for Expert Systems and Robotics, The Florida State University, Tallahassee, FL

  • Venue:
  • Fuzzy Sets and Systems
  • Year:
  • 1989

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Abstract

The system based on the fuzzy set theory for forecasting the Florida corporate income tax revenue was developed in response to several years of unsuccessful forecasts generated by the conventional econometric methods. The system consists of two parts: 1. (a) The first part utilizes time series of revenue, and of the real per capita GNP. Both time series are transformed into trend vectors through the utilization of moving average technique. Both vectors are divided into strings of growth patterns such as: 'accelerating growth', 'decelerating growth', and 'negative growth'. The system checks for several indications of systematic relationship between the GNP vector and the tax revenue vector. Based on that relationship, the forecast of the corporate tax revenues is generated in fuzzy, such as: 'very rapid growth', 'slightly negative growth', etc. 2. (b) The fuzzy forecast from the first part of the system constitutes input into the second part, which in turn generates the range of forecasted revenue in millions of dollars. A control mechanism which is built into the system continously checks forecasted rates of change in tax revenues against the actuals throughout the history of the time-series to make sure that the cumulative forecasting error will not reach unacceptable magnitude.