A new method of time series forecasting using intuitionistic fuzzy set based on average-length

ABSTRACT

The major problem in the field of fuzzy time series (FTS) is the accuracy rate in the forecasted values. To overcome this problem here, we propose a model for intuitionistic FTS forecasting based on average-length of interval, which enhances the forecasting result. The proposed model is focused on how to fuzzify the historical time series data. Here, the fuzzification of each observation is intuitionistic fuzzification, which is based on the maximum degree of score function and also establishes intuitionistic fuzzy logical relationships (IFLR) among all intuitionistic fuzzified data set. Here, we use simple arithmetic computations in defuzzification process with measuring the frequency of IFLR. An illustrative example of enrollments at the University of Alabama is used to verify the effectiveness of the proposed model and comparison in terms of RMSE and AFE with some of the existing forecasting models to show its superiority.

KEYWORDS:

• Fuzzy time series
• intuitionistic fuzzy set
• score function
• frequency occurrence
• Intuitionistic fuzzy
• logical relationships

Acknowledgments

The authors are very thankful to the editor and the anonymous reviewers for their constructive suggestions to improve the quality of this article. First author dedicated this research article to our supervisor Late Prof. S.R. Singh.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Abhishekh

Abhishekh is an Assistant Professor in the department of Mathematics, Vishwavidyalaya Engineering College, Ambikapur, Chhattisgarh-India. He has obtained his Ph.D. degree in Mathematics from Institute of Science, Banaras Hindu University, Varanasi India. His current research areas are fuzzy optimization, multi-factor fuzzy time series, type-2 fuzzy set and intuitionistic fuzzy set. He has published more than 18 research articles in various peer reviewed International Journals. He is an editorial board member of SCIREA Journal of Mathematics and reviewer of several peer reviewed indexed Journals as Elsevier, Springer etc.

Surendra Singh Gautam

Surendra Singh Gautam is lecturer in Government Polytechnic College, Gariyaband, Chhattisgarh-India. He has received his M.Sc. degree in Mathematics from University of Allahabad, Prayagraj-India in 2012 and also obtained his Ph.D. degree in Mathematics from Institute of Science, Banaras Hindu University, Varanasi India in 2018. His present research interests include applications of hybrid soft computing models in time series forecasting and decision making problem.

S. R. Singh

S. R. Singh was worked as a professor in Department of Mathematics, Banaras Hindu University, Varanasi-India. He received his M.Sc. and Ph.D. degree in Mathematics from Banaras Hindu University, Varanasi, India. Also, he was Head of the Department in 2016 and then after he passed away in 2017. His research areas were includes computational optimization, soft computing techniques, fuzzy mathematics, and decision-making problems. He had published more than 50 research publications in refereed Journals. Twelve students have completed their Ph.D. degree under his supervision. He was also a reviewer of several Elsevier, Springer, and other journals. He was also a member of different prestigious societies, like Operational Research Society of India and Association of Inventory Academicians and Practitioners.