An improved fuzzy time-series method of forecasting based on L–R fuzzy sets and its application

Abstract

Classical time-series theory assumes values of the response variable to be ‘crisp’ or ‘precise’, which is quite often violated in reality. However, forecasting of such data can be carried out through fuzzy time-series analysis. This article presents an improved method of forecasting based on L–R fuzzy sets as membership functions. As an illustration, the methodology is employed for forecasting India's total foodgrain production. For the data under consideration, superiority of proposed method over other competing methods is demonstrated in respect of modelling and forecasting on the basis of mean square error and average relative error criteria. Finally, out-of-sample forecasts are also obtained.

Keywords:

• foodgrain production
• fuzzy logical relations
• fuzzy time-series
• L–R fuzzy sets
• membership functions

Acknowledgements

Authors are grateful to the referees for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.