AN EFFECTIVE LENGTH OF INTERVALS FOR FORECASTING IN FUZZY TIME SERIES - Suriana Lasaraiya; Darmesah Gabda; Che Haziqah Che Hussin; Arif Mandangan (Journal of Islamic, Social, Economics and Development, 2022)
The research on forecasting in fuzzy time series has increased due to its capabilityin dealing with uncertainty and vagueness. However, the effectiveness of the forecasting ishugely depending on the first step in every forecasting model being applied, that is thedetermination of the size of the intervals. However, the study on how to determine the length ofintervals has rarely been discussed. This study suggests three different methods on choosingthe best size of interval in fuzzy time series to increase the accuracy of forecasting in fuzzy timeseries. The three methods proposed are the average-based, half of the average based andnatural partitions on 345 rules. We apply those methods to the selected data of tuberculosisreported monthly in Sabah starting from January 2012 until May 2020. The data is collectedfrom the Queen Elizabeth Hospital, Kota Kinabalu, Sabah. According to performanceevaluation in terms of MSE and RMSE values, both suggest that the length of intervaldetermined by average-based length is the most accurate compared to the other two methods.