Forecasting Unemployment based on Fuzzy Time Series with Different Degree of Confidence

Authors

  • Siti Musleha Ab Mutalib Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Pahang, Pahang, Malaysia
  • Nazirah Ramli Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia
  • Daud Mohamad Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Pahang, Pahang, Malaysia

Keywords:

Fuzzy Forecasting, Unemployment, Trapezoidal Fuzzy Number, Statistical Distribution,

Abstract

Unemployment prediction has attracted much attention to many sectors as it can be a guide for decision making planning. In the last few years, many different forecasting techniques based on fuzzy time series (FTS) have been designed. However, most of the models used the discrete fuzzy set as a base for calculating the predicted values and the discrete fuzzy set cannot provide the forecasted range under different degree of confidence (DDoC). In this paper, FTS with trapezoidal fuzzy numbers and frequency density based partitioning approach is introduced for predicting unemployment. This model is an enhancement of the previous FTS models as it can produce the forecasted ranges under DDoC which can provide more information on the forecasted values.

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Published

2017-03-15

How to Cite

Ab Mutalib, S. M., Ramli, N., & Mohamad, D. (2017). Forecasting Unemployment based on Fuzzy Time Series with Different Degree of Confidence. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-4), 21–24. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1769

Issue

Vol. 9 No. 1-4: Smart City Technology in Communication and Computer Engineering II

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Articles

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