کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495156 862817 2015 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables
ترجمه فارسی عنوان
یک الگوریتم پیش بینی مبتنی بر خوشه بندی برای سری زمانی فازی چند متغیره با استفاده از ترکیب خطی متغیرهای مستقل
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• A clustering based algorithm with linear combinations of the variables is presented.
• Uncertainty and ambiguity of the data is handled by the fuzzy clusters.
• Linear combinations of the variables allow more learning from the available samples.
• The presented algorithm is faster and more accurate than the conventional algorithms.

There are two popular types of forecasting algorithms for fuzzy time series (FTS). One is based on intervals of universal sets of independent variables and the other is based on fuzzy clustering algorithms. Clustering based FTS algorithms are preferred since role and optimal length of intervals are not clearly understood. Therefore data of each variable are individually clustered which requires higher computational time. Fuzzy Logical Relationships (FLRs) are used in existing FTS algorithms to relate input and output data. High number of clusters and FLRs are required to establish precise input/output relations which incur high computational time. This article presents a forecasting algorithm based on fuzzy clustering (CFTS) which clusters vectors of input data instead of clustering data of each variable separately and uses linear combinations of the input variables instead of the FLRs. The cluster centers handle fuzziness and ambiguity of the data and the linear parts allow the algorithm to learn more from the available information. It is shown that CFTS outperforms existing FTS algorithms with considerably lower testing error and running time.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 35, October 2015, Pages 151–160
نویسندگان
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