A flexible fuzzy regression algorithm for forecasting oil consumption estimation

By A. Azadeh, M. Khakestani, Morteza Saberi. 2009

49 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/02a2032752fa4fd362dd49c714b21223d3d8c1d9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cheng-Lung Tseng
2. Hsin-Chia Fu
3. H. Pao

In Management Science and Operations Research

1. Hsin-Chia Fu
2. Cheng-Lung Tseng
3. Mahmoud Omid
4. A. Keyhani
5. B. Khoshnevisan
6. H. Pao
7. Usama Al-mulali
8. Juha Itkonen

In Statistics and Probability

1. Cheng-Lung Tseng
2. Hsin-Chia Fu
3. H. Pao

Citations in the corpus

• Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model (2012, methodology): “Due to fluctuations in energy consumption, some intelligent nonlinear forecasting methods, namely artificial neural network (ANN) [26e28], fuzzy regression [29,30], and some hybrid models [31,32], have been employed to predict energy demand more efficiently.”
• A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran (2014, methodology): “formulated a flexible fuzzy regression model to forecast oil consumption based on standard economic indicators (Azadeh et al., 2009).”