Approximating The Effective Length of Interval to Forecast in Fuzzy Time Series

  • Suriana L Preparatory Centre for Science and Technology, University Malaysia Sabah, 88400 UMS Kota Kinabalu Sabah
  • Suzelawati Z. Faculty of Science and Natural Resources, University Malaysia Sabah, 88400 UMS Kota Kinabalu Sabah
  • Risman M. H. Department of Mathematics and Statistics, Faculty of Science, University of Putra Malaysia, 43400 UPM Serdang, Selangor
  • Amirul A. Azman Hashim International Business School, University of Technology Malaysia, 81310 UTM Johor Bahru, Johor.


The research on forecasting in fuzzy time series has increased due to its capability in dealing with uncertainty and vagueness. However, in this research the effectiveness of the forecasting is hugely depending on the first step in every forecasting model being applied, that is the determination of the size of the intervals. However, previous study did not mention on the best length of interval to be used in the model. In this study, we suggested a few different lengths of interval to be used, to look for the best size of interval in fuzzy time series. The aim is to increase the accuracy of forecasting. This method is applied to the selected data of tuberculosis cases reported monthly in Sabah starting from January 2012 until May 2020. The data is collected from the Queen Elizabeth Hospital in Kota Kinabalu, Sabah. The performance of evaluations is showed by comparison on the values obtained for MSE and RMSE. One numerical data set from the whole tuberculosis data were used to illustrate the chosen methods.    


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How to Cite
L, Suriana et al. Approximating The Effective Length of Interval to Forecast in Fuzzy Time Series. Menemui Matematik (Discovering Mathematics), [S.l.], v. 44, n. 2, p. 127-138, dec. 2022. ISSN 0126-9003. Available at: <>. Date accessed: 22 sep. 2023.

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