Time Series Forecasting Using Artificial Neural Networks vs. Evolving Models

被引：0

作者：

Antonio Iglesias, Jose ^{[1
]}

Gutierrez, German ^{[1
]}

Ledezma, Agapito ^{[1
]}

Sanchis, Araceli ^{[1
]}

机构：

[1] Univ Carlos III Madrid, Madrid, Spain

来源：

2014 IEEE CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS (EAIS) | 2014年

关键词：

FUZZY; IDENTIFICATION; APPROXIMATION; PREDICTION; MULTISTEP; SYSTEMS;

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：

Time series forecasting plays an important role in many fields such as economics, finance, business intelligence, natural sciences, and the social sciences. This forecasting task can be achieved by using different techniques such as statistical methods or Artificial Neural Networks (ANN). In this paper, we present two different approaches to time series forecasting: evolving Takagi-Sugeno (eTS) fuzzy model and ANN. These two different methods will be compared taking into account the different characteristic of each approach.

引用

页数：7

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