An Approach to Represent Time Series Forecasting via Fuzzy Numbers

This paper introduces a new approach for estimating the uncertainty in the forecast through the construction of Triangular Fuzzy Numbers (TFNs). The interval of the proposed TFN presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE). Here, instead of the represent...

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Bibliographic Details
Published in:	2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation pp. 51 - 56
Main Authors:	Sahin, Atakan, Kumbasar, Tufan, Yesil, Engin, Doydurka, M. Furkan, Karasakal, Onur
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2014
Subjects:	Accuracy forecasting fuzzy estimator Fuzzy logic fuzzy numbers fuzzy time series Mathematical model Predictive models Time series analysis Uncertainty Upper bound
Online Access:	Get full text
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Summary:	This paper introduces a new approach for estimating the uncertainty in the forecast through the construction of Triangular Fuzzy Numbers (TFNs). The interval of the proposed TFN presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE). Here, instead of the representing the forecast with a crisp value with a Prediction Interval (PI), the level of uncertainty associated with the point forecasts will be quantified by defining TFNs (linguistic terms) within the uncertainty interval provided by the FLUBE. This will give the opportunity to handle the forecast as linguistic terms which will increase the interpretability. Moreover, the proposed approach will provide valuable information about the accuracy of the forecast by providing a relative membership degree. The demonstrated results indicate that the proposed FLUBE based TFN representation is an efficient and useful approach to represent the uncertainty and the quality of the forecast.
DOI:	10.1109/AIMS.2014.36