Maximum-Minimum Temperature Prediction Using Fuzzy Random Auto-Regression Time Series Model

被引：0

作者：

Efendi, Riswan ^{[1
,2
]}

Samsudin, Noor Azah ^{[1
]}

Arbaiy, Nureize ^{[1
]}

Deris, Mustafa Mat ^{[1
]}

机构：

[1] Univ Tun Hussein Onn Malaysia, Fac Comp Sci & Informat Technol, Batu Pahat, Malaysia

[2] UIN Sultan Syarif Kasim, Math Dept, Pekanbaru, Indonesia

来源：

2017 5TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI) | 2017年

关键词：

component; fuzzy random variable; temperature; min-max procedure; auto-regression model; LOGICAL RELATIONSHIPS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many models have been suggested to predict the weather and temperature data. Most of them used the single point data in building prediction equations. Besides that, the randomness, the vagueness and possibility of the temperature data are also not much concerned. In this paper, we introduce the minimum-maximum procedure for daily temperature modeling based on fuzzy random auto-regression time series. The proposed procedure was able to cover the variability of the temperature in nature. The result showed that mean square error of proposed model is smaller than the existing models.

引用

页码：57 / 60

页数：4

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