An Approach to Represent Time Series Forecasting via Fuzzy Numbers
- Resource Type
- Conference
- Authors
- Sahin, Atakan; Kumbasar, Tufan; Yesil, Engin; Doydurka, M. Furkan; Karasakal, Onur
- Source
- 2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation Artificial Intelligence, Modelling and Simulation (AIMS), 2014 2nd International Conference on. :51-56 Nov, 2014
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Uncertainty
Predictive models
Upper bound
Accuracy
Mathematical model
Time series analysis
Fuzzy logic
forecasting
fuzzy time series
fuzzy numbers
fuzzy estimator
- Language
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.