Affiliations: [a] School of Economics and Statistics, Guangzhou University, Guangzhou, China
| [b] School of Computer Science and Technology, Guangzhou University, Guangzhou, China
| [c] College of Agriculture Economics and Management, Jilin Agriculture University, China
Correspondence:
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Corresponding author. Chongqi Zhang, School of Economics and Statistics, Guangzhou University, Guangzhou, China. E-mail: [email protected].
Abstract: Time series is a classification of data series of variables, orderly arranged with respect to time. In time series analysis, forecasting is the vital area of study besides other meaningful characteristics of the data. It has vast application in decision-making and prediction in the domain of economics, agriculture, medicine, industries, energy sector and other sciences. Fuzzy time series emerged as a robust tool to cater for historical data in linguistic values. This paper proposes the new method of fuzzy time series forecasting based on the approach of fuzzy clustering and information granules integrated with the weighted average approach to deal with the uncertainty in data series. To distinguish the power of modeling and prediction, the strategies of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized as a criterion. Findings illustrate that proposed fuzzy based time series approach is vigorous to compute the accurate estimates.
Keywords: Fuzzy set, fuzzy c-means clustering, information granules, fuzzy time series, forecasting
