Comparison of fuzzy time series forecasting model based on similarity measure concept with different types of interval length

Fuzzy time series forecasting model has been proposed to cater for data in linguistic values. One of the crucial factors that influence the performance of fuzzy time series is the partition of interval length. This paper compares the effect of several interval lengths to the performance of fuzzy tim...

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Bibliographic Details
Main Authors: Ramli, Nazirah, Alam, Nik Muhammad Farhan Hakim Nik Badrul, Mutalib, Siti Musleha Ab, Mohamad, Daud
Format: Conference Proceeding
Language:English
Subjects:
Forecasting
Mathematical models
Model accuracy
Similarity
Similarity measures
Time series
Citations: Items that cite this one
Online Access:Get full text
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Summary:Fuzzy time series forecasting model has been proposed to cater for data in linguistic values. One of the crucial factors that influence the performance of fuzzy time series is the partition of interval length. This paper compares the effect of several interval lengths to the performance of fuzzy time series forecasting model. The interval length considered are the average based, frequency density based and randomly chosen length methods. The data are represented in trapezoidal fuzzy numbers and the accuracy of the forecasting model is calculated using the distance, area, height and perimeter ratio similarity measure. The model is applied in a numerical example of Malaysian unemployment rate. The findings show that the average based length outperforms the other two types of interval length.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0018091