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

 
 
 
  • Abstract
  • Keywords
  • References
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  • 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


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




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