Fuzzy Transforms and Seasonal Time Series

被引：1
|
作者：
Di Martino, Ferdinando [1 ]
Sessa, Salvatore [1 ]
机构：
[1] Univ Napoli Federico II, Dipartimento Architettura, Via Toledo 402, I-80134 Naples, Italy
来源：
FUZZY LOGIC AND SOFT COMPUTING APPLICATIONS, WILF 2016 | 2017年 / 10147卷
关键词：
ARIMA; Average seasonal variation; Forecasting; Fuzzy transform; Time series; NEURAL-NETWORKS;
D O I：
10.1007/978-3-319-52962-2_4
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Like in our previous papers, we show the trend of seasonal time series by means of polynomial interpolation and we use the inverse fuzzy transform for prediction of the value of an assigned output. As example, we use the daily weather dataset of the city of Naples (Italy) starting from data collected from 2003 till to 2015 making predictions on the the relative humidity parameter. We compare our method with the traditional F-transform based, the average seasonal variation and the famous ARIMA methods.
引用
收藏
页码：54 / 62
页数：9
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