A new intuitionistic fuzzy functions approach based on hesitation margin for time-series prediction

Yolcu, Özge Cağcağ; Baş, Eren; Eğrioğlu, Erol; Yolcu, Ufuk

doi:10.1007/s00500-019-04432-2

Cited by 17 publications

(4 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar work on fuzzy time series can be found in Lee, et al (2001) [13] and Tsai and Wu (1999) [14]. The other researchers such as Liu (2007) [15], Ozge, et al (2020) [16] and Lee and Chou (2007) [17] does not specify on how they find the suitable the length of interval in their proposed methods. More research on defining the suitable interval also found in Chen (2014) [18], Wang et.al (2015) [19] as well as Lu et al (2015) [20] in their proposed model.…”

Section: Introductionmentioning

confidence: 70%

“…be the universe of discourse, where . A fuzzy set of of is defined by (1) where is the membership function of the fuzzy set , . is the element of fuzzy set , and is the degree of belongingness of to for and .…”

Section: Letmentioning

confidence: 99%

“…In all these fields, obtaining an accurate picture of the future is an essential requirement and this can only be possible with an appropriate and competent prediction tool. Since the main concern of the time series analysis is to predict the future, it can be considered as an inference problem [1]. We identify that there are three main kinds of inference system (IS) which are utilized in time-series analysis as a prediction tool, i.e Statistical Inference System (SIS), Fuzzy Inference System (FIS) and computational inference system (CIS).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Implementation of Revised Heuristic Knowledge in Average-based Interval for Fuzzy Time Series Forecasting of Tuberculosis Cases in Sabah

Lasaraiya¹,

Zenian²,

Hasim³

et al. 2023

IJACSA

View full text Add to dashboard Cite

Fuzzy time series forecasting is one method used to forecast in certain reality problems. The research on fuzzy time series forecasting has been increased due to its capability in dealing with vagueness and uncertainty. In this paper, we are dealing with implementation of revised heuristic knowledge to basic average-based interval and showing that these models forecast better than the basic one. We suggest three different lengths of interval, size 5, size 10 and size 20 to be used in comparing these models of average-based interval, average-based interval with implementation of heuristic knowledge and, average-based interval with implementation of revised heuristic knowledge. These models applied to forecast the number of tuberculosis cases reported monthly in Sabah starting from January 2012 until December 2019. A few numerical examples are shown as well. The performances of evaluations are shown by comparison on the values obtained by Mean Square error (MSE) and Root Mean Square Error (RMSE).

show abstract

Section: Introductionmentioning

confidence: 70%

Section: Letmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Implementation of Revised Heuristic Knowledge in Average-based Interval for Fuzzy Time Series Forecasting of Tuberculosis Cases in Sabah

Lasaraiya¹,

Zenian²,

Hasim³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…On the other hand, for stock market prediction, a fuzzy inference system based on intuitionistic time series has been introduced (Egrioglu et al, 2019a). In some studies, different real-world time series have been analyzed by utilizing intuitionistic fuzzy functions (Cagcag Yolcu et al, 2020;Kizilaslan et al, 2020). Besides these approaches, to predict network traffic long-term intuitionistic fuzzy time series has been presented (Fan et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%