期刊
ANNALS OF OPERATIONS RESEARCH
卷 312, 期 1, 页码 473-493出版社
SPRINGER
DOI: 10.1007/s10479-021-04041-z
关键词
Forecast; Fuzzy cluster analysis; Interpolate; Model; Time series
资金
- Ministry of Education and Training in Vietnam [B2021 - TCT - 01]
This article presents a new fuzzy time series model that improves historical data interpolation and future forecasting. By using an enhanced fuzzy cluster analysis algorithm, the efficiency and accuracy of the algorithm is improved, and its superiority is demonstrated across multiple datasets.
This article proposes a new fuzzy time series model that can interpolate historical data, and forecast effectively for the future. It is combination of the improved steps from the existing models. There are problems to use the percentage variations of series between consecutive periods of time as a universal set, to divide the universal set into clusters by the automatic algorithm based on the similarity between elements, to determine the relationships between elements in the series and the divided clusters by the improved fuzzy cluster analysis algorithm, and to interpolate the historical data and to forecast for future by new principle. The proposed algorithm is performed quickly and efficiently by the established Matlab procedure. It is illustrated by an example, and tested for many other data sets, especially for 3003 series in M3-Competition data. Comparing to the existing models, the proposed model always gives the best result. We also apply the proposed model in forecasting the salty peak for a coastal province of Vietnam. Examples and application show the potential of the studied problem.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据