An Approach to Represent Time Series Forecasting via Fuzzy Numbers

Şahin, Atakan; Kumbasar, Tufan; Yeşil, Engin; Doydurka, M. Furkan; Karasakal, Onur

doi:10.1109/aims.2014.36

Cited by 7 publications

(13 citation statements)

References 15 publications

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“…Thus, the generated FLUBE might result with a general approximation while neglecting the characteristics of the error values. Therefore, P is an important design parameter and needs to be tuned for each handled data set [13]. To illustrate this concept, for the Data Set-1, the performance of the FLUBE is illustrated in Fig.…”

Section: A Design Of the Flubementioning

confidence: 99%

“…In this section, we will firstly present the FLUBE which will define the uncertainty interval of the single point forecast value [13]. Then instead of conventional PI representation, the level of uncertainty associated with the point forecasts will be quantified by defining TFNs within the uncertainty interval provided by the FLUBE.…”

Section: Forecast Representation Of Tfnmentioning

confidence: 99%

“…The FLUBE is constructed by choosing the input to be the target data and the output as the forecast error terms [13]. The required data sets are chosen by the Selection Algorithm (SA).…”

Section: A Design Of the Flubementioning

confidence: 99%

“…Thus, the construction of PIs has been a subject of much attention [6]. Thus, different methods haven been proposed for the construction of PIs such as delta technique [7,8], Bayesian technique [9], bootstrap [10], mean-variance estimation [11], lower and upper bound estimation method [12] Recently, a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE) is proposed to estimate the uncertainty in the forecast [13]. Then, the FLUBE is used for the Linguistic Term Generation and Representation Approach (LinGRA) so that the forecasts are represented with the linguistic terms, which are defined with Triangular Fuzzy Numbers (TFNs).…”

mentioning

confidence: 99%

“…Then, the FLUBE is used for the Linguistic Term Generation and Representation Approach (LinGRA) so that the forecasts are represented with the linguistic terms, which are defined with Triangular Fuzzy Numbers (TFNs). In the LinGRA, the support of the TFN is constructed on the output interval generated by the FLUBE while its center is directly assigned to the forecast value [13].…”

mentioning

confidence: 99%

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An Enhanced Fuzzy Linguistic Term Generation and Representation for time series forecasting

Şahin

Kumbasar

Yeşil

et al. 2015

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

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This version is available at https://strathprints.strath.ac.uk/60316/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.  Abstract-This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation andRepresentation 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.

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Section: A Design Of the Flubementioning

confidence: 99%

Section: Forecast Representation Of Tfnmentioning

confidence: 99%

“…The FLUBE is constructed by choosing the input to be the target data and the output as the forecast error terms [13]. The required data sets are chosen by the Selection Algorithm (SA).…”

Section: A Design Of the Flubementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An Enhanced Fuzzy Linguistic Term Generation and Representation for time series forecasting

Şahin

Kumbasar

Yeşil

et al. 2015

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

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An interval fuzzy number-based fuzzy collaborative forecasting approach for DRAM yield forecasting

Chen

Chiu

2020

Complex Intell. Syst.

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Most existing fuzzy collaborative forecasting (FCF) methods adopt type-1 fuzzy numbers to represent fuzzy forecasts. FCF methods based on interval-valued fuzzy numbers (IFNs) are not widely used. However, the inner and outer sections of an IFN-based fuzzy forecast provide meaning information that serves different managerial purposes, which is a desirable feature for a FCF method. This study proposed an IFN-based FCF approach. Unlike existing IFN-based fuzzy association rules or fuzzy inference systems, the IFN-based FCF approach ensures that all actual values fall within the corresponding fuzzy forecasts. In addition, the IFN-based FCF approach optimizes the forecasting precision and accuracy with the outer and inner sections of the aggregation result, respectively. Based on the experimental results, the proposed FCF-II approach surpassed existing methods in forecasting the yield of a dynamic random access memory product.

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Impacts of Emissions Trading on a Company Under Uncertain Multi-Period Environment

Zapletal

2019

Fuzzy Information and Engineering

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Emissions trading within the Emissions Trading Scheme of the European Union (EU ETS) influences European industrial companies since 2005. The companies must choose their strategy, how to reduce the costs of emissions allowances. The changing system's conditions and volatile price of allowances make this decision very tough. Moreover, the effect of this system on companies still increases as the policy-makers want to increase its efficiency, and the emission price increases. Despite several studies devoted to the relationship between the EU ETS and companies have already been published, there is still a gap in this field. Authors usually use the stochastic approach, which is not always applicable and it reveals the expected future result. In this paper, the potential range of impact of emissions trading is explored. Namely, the multi-period fuzzy possibilistic programming model is developed to reveal possible costs of emissions trading, optimal strategy on allowances and effect of banking the allowances for hedging against the risk. The model is verified using the data of a real-life industrial company. The results show that the costs of emissions trading are perceptible for the company and they can be reduced by up to 6% using banking.

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An Approach to Represent Time Series Forecasting via Fuzzy Numbers

Cited by 7 publications

References 15 publications

An Enhanced Fuzzy Linguistic Term Generation and Representation for time series forecasting

An Enhanced Fuzzy Linguistic Term Generation and Representation for time series forecasting

An interval fuzzy number-based fuzzy collaborative forecasting approach for DRAM yield forecasting

Impacts of Emissions Trading on a Company Under Uncertain Multi-Period Environment

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