التفاصيل البيبلوغرافية
| العنوان: |
Cardinality-Based Fuzzy Time Series for Forecasting Enrollments. |
| المؤلفون: |
Carbonell, Jaime G., Siekmann, Jörg, Okuno, Hiroshi G., Ali, Moonis, Jing-Rong Chang, Ya-Ting Lee, Shu-Ying Liao, Ching-Hsue Cheng |
| المصدر: |
New Trends in Applied Artificial Intelligence; 2007, p735-744, 10p |
| مستخلص: |
Forecasting activities are frequent and widespread in our life. Since Song and Chissom proposed the fuzzy time series in 1993, many previous studies have proposed variant fuzzy time series models to deal with uncertain and vague data. A drawback of these models is that they do not consider appropriately the weights of fuzzy relations. This paper proposes a new method to build weighted fuzzy rules by computing cardinality of each fuzzy relation to solve above problems. The proposed method is able to build the weighted fuzzy rules based on concept of large itemsets of Apriori. The yearly data on enrollments at the University of Alabama are adopted to verify and evaluate the performance of the proposed method. The forecasting accuracies of the proposed method are better than other methods. [ABSTRACT FROM AUTHOR] |
|
Copyright of New Trends in Applied Artificial Intelligence is the property of Springer eBooks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Supplemental Index |
Full Text Finder