Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    This paper proposes an enhanced fuzzy time series (FTS) prediction model that can keep some information under a various level of confidence throughout the forecasting procedure. The forecasting accuracy is developed based on the similarity between the fuzzified historical data and the fuzzy forecast values. No defuzzification process involves in the proposed method. The frequency density method is used to partition the interval, and the area and height type of similarity measure is utilized to get the forecasting accuracy. The proposed model is applied in a numerical example of the unemployment rate in Malaysia. The results show that on average 96.9% of the forecast values are similar to the historical data. The forecasting error based on the distance of the similarity measure is 0.031. The forecasting accuracy can be obtained directly from the forecast values of trapezoidal fuzzy numbers form without experiencing the defuzzification procedure.

  • Keywords

    Area and Height Similarity Measure; Forecasting Accuracy; Frequency Density; Fuzzy Time Series; Unemployment Rate.

  • References

      [1] Song Q & Chissom BS (1993), Forecasting enrollments with fuzzy time series – Part I. Fuzzy Sets and Systems 54, 1-9.

      [2] Yu HK (2005), A refined fuzzy time series model for forecasting. Physica A: Statistical Mechanics and its Application 346(3-4), 657-681.

      [3] Liu HT & Wei ML (2010), An improved fuzzy forecasting method for seasonal time series. Expert Systems with Applications 39(9), 6310-6318.

      [4] Qiu W, Zhang P & Wang Y (2015), Fuzzy time series forecasting model based on automatic clustering techniques and generalized fuzzy logical relationship. Mathematical Problems in Engineering, 1−8.

      [5] Liu HT (2007), An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers. Fuzzy Optimization and Decision Making 6(1), 63-80.

      [6] Liu HT (2009), An integrated fuzzy time series forecasting system. Expert Systems with Applications 36 (6), 10045-10053.

      [7] Hsu CC & Chen SM (2002), A new method for forecasting enrollments based on fuzzy time series. Proceedings of the Seventh Conference on Artificial Intelligence and Applications, 17-22.

      [8] Patra K & Modal SK (2015), Fuzzy risk analysis using area and height based similarity measure on generalized trapezoidal fuzzy numbers and its application. Applied Soft Computing 28, 276-284.

      [9] Song Q & Chissom BS (1994), Forecasting enrollments with fuzzy time series – Part II. Fuzzy Sets and Systems 62, 1-8.

      [10] Wang X (1997), An investigation into relations between some transitivity related concept. Fuzzy sets and Systems 89(2), 257-262.

      [11] Cheng C, Wang J & Li C (2008), Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix. Expert Systems with Applications 34(4), 2568-2575.

      [12] Department of Statistic Malaysia. Time series data of unemployment. https://www.dosm.gov.my. Accessed January 13, 2014.

      [13] Lewis CD, Industrial and business forecasting methods, Butterworths, London, (1982).




Article ID: 22284
DOI: 10.14419/ijet.v7i4.30.22284

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.