An enhanced fuzzy linguistic term generation and representation for time series forecasting

Atakan Sahin, Tufan Kumbasar, Engin Yesil, M. Furkan Dodurka, Onur Karasakal, Sarven Siradag

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

24 Downloads (Pure)

Abstract

This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE) by getting the models of forecast errors. Thus, instead of a classical statistical approaches, the level of uncertainty associated with the point forecasts will be defined within the FLUBE bounds and these bound can be used for defining fuzzy linguistic terms for the forecasts. Here, ElinGRA is proposed to generate triangular fuzzy numbers (TFNs) for the predictions. In addition to opportunity to handle the forecast as linguistic terms which will increase the interpretability, ElinGRA improved forecast accuracy of constructed TFNs by adding an extra correction term. The results of the experiments, which are conducted on two data sets, show the benefit of using ElinGRA to represent the uncertainty and the quality of the forecast.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Number of pages8
ISBN (Print)9781467374286
DOIs
Publication statusPublished - 30 Nov 2015
EventIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey
Duration: 2 Aug 20155 Aug 2015

Conference

ConferenceIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
CountryTurkey
CityIstanbul
Period2/08/155/08/15

Fingerprint

Time Series Forecasting
Linguistics
Forecast
Time series
Term
Triangular Fuzzy number
Fuzzy logic
Fuzzy Logic
Upper and Lower Bounds
Estimator
Uncertainty
Prediction
Interpretability
Margin
Error Bounds
Enhancement
Interval

Keywords

  • forecasting
  • fuzzy estimator
  • fuzzy linguistic terms
  • fuzzy numbers
  • fuzzy time series
  • prediction Interval

Cite this

Sahin, A., Kumbasar, T., Yesil, E., Dodurka, M. F., Karasakal, O., & Siradag, S. (2015). An enhanced fuzzy linguistic term generation and representation for time series forecasting. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015) Piscataway, NJ.: IEEE. https://doi.org/10.1109/FUZZ-IEEE.2015.7337904
Sahin, Atakan ; Kumbasar, Tufan ; Yesil, Engin ; Dodurka, M. Furkan ; Karasakal, Onur ; Siradag, Sarven. / An enhanced fuzzy linguistic term generation and representation for time series forecasting. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015). Piscataway, NJ. : IEEE, 2015.
@inproceedings{8b55dd31510c4a2c89e8bbb3142f9f91,
title = "An enhanced fuzzy linguistic term generation and representation for time series forecasting",
abstract = "This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE) by getting the models of forecast errors. Thus, instead of a classical statistical approaches, the level of uncertainty associated with the point forecasts will be defined within the FLUBE bounds and these bound can be used for defining fuzzy linguistic terms for the forecasts. Here, ElinGRA is proposed to generate triangular fuzzy numbers (TFNs) for the predictions. In addition to opportunity to handle the forecast as linguistic terms which will increase the interpretability, ElinGRA improved forecast accuracy of constructed TFNs by adding an extra correction term. The results of the experiments, which are conducted on two data sets, show the benefit of using ElinGRA to represent the uncertainty and the quality of the forecast.",
keywords = "forecasting, fuzzy estimator, fuzzy linguistic terms, fuzzy numbers, fuzzy time series, prediction Interval",
author = "Atakan Sahin and Tufan Kumbasar and Engin Yesil and Dodurka, {M. Furkan} and Onur Karasakal and Sarven Siradag",
year = "2015",
month = "11",
day = "30",
doi = "10.1109/FUZZ-IEEE.2015.7337904",
language = "English",
isbn = "9781467374286",
booktitle = "2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015)",
publisher = "IEEE",

}

Sahin, A, Kumbasar, T, Yesil, E, Dodurka, MF, Karasakal, O & Siradag, S 2015, An enhanced fuzzy linguistic term generation and representation for time series forecasting. in 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015). IEEE, Piscataway, NJ., IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015, Istanbul, Turkey, 2/08/15. https://doi.org/10.1109/FUZZ-IEEE.2015.7337904

An enhanced fuzzy linguistic term generation and representation for time series forecasting. / Sahin, Atakan; Kumbasar, Tufan; Yesil, Engin; Dodurka, M. Furkan; Karasakal, Onur; Siradag, Sarven.

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015). Piscataway, NJ. : IEEE, 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - An enhanced fuzzy linguistic term generation and representation for time series forecasting

AU - Sahin, Atakan

AU - Kumbasar, Tufan

AU - Yesil, Engin

AU - Dodurka, M. Furkan

AU - Karasakal, Onur

AU - Siradag, Sarven

PY - 2015/11/30

Y1 - 2015/11/30

N2 - This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE) by getting the models of forecast errors. Thus, instead of a classical statistical approaches, the level of uncertainty associated with the point forecasts will be defined within the FLUBE bounds and these bound can be used for defining fuzzy linguistic terms for the forecasts. Here, ElinGRA is proposed to generate triangular fuzzy numbers (TFNs) for the predictions. In addition to opportunity to handle the forecast as linguistic terms which will increase the interpretability, ElinGRA improved forecast accuracy of constructed TFNs by adding an extra correction term. The results of the experiments, which are conducted on two data sets, show the benefit of using ElinGRA to represent the uncertainty and the quality of the forecast.

AB - This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE) by getting the models of forecast errors. Thus, instead of a classical statistical approaches, the level of uncertainty associated with the point forecasts will be defined within the FLUBE bounds and these bound can be used for defining fuzzy linguistic terms for the forecasts. Here, ElinGRA is proposed to generate triangular fuzzy numbers (TFNs) for the predictions. In addition to opportunity to handle the forecast as linguistic terms which will increase the interpretability, ElinGRA improved forecast accuracy of constructed TFNs by adding an extra correction term. The results of the experiments, which are conducted on two data sets, show the benefit of using ElinGRA to represent the uncertainty and the quality of the forecast.

KW - forecasting

KW - fuzzy estimator

KW - fuzzy linguistic terms

KW - fuzzy numbers

KW - fuzzy time series

KW - prediction Interval

UR - http://www.scopus.com/inward/record.url?scp=84975743610&partnerID=8YFLogxK

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7329077

U2 - 10.1109/FUZZ-IEEE.2015.7337904

DO - 10.1109/FUZZ-IEEE.2015.7337904

M3 - Conference contribution book

AN - SCOPUS:84975743610

SN - 9781467374286

BT - 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015)

PB - IEEE

CY - Piscataway, NJ.

ER -

Sahin A, Kumbasar T, Yesil E, Dodurka MF, Karasakal O, Siradag S. An enhanced fuzzy linguistic term generation and representation for time series forecasting. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015). Piscataway, NJ.: IEEE. 2015 https://doi.org/10.1109/FUZZ-IEEE.2015.7337904