The application of neural networks to forecast fuzzy time series

By K. Huarng, Tiffany Hui-Kuang Yu. 2006

325 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5deb01003d5edc78293153b3f800d3b4b0f44dc0

How researchers see this paper

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

1. Chih-Feng Liu
2. Chi-Yuan Yeh
3. Shie-Jue Lee
4. Mehdi Khashei
5. Mehdi Bijari
6. Ching-Hsue Cheng
7. G. R. Ardali
8. Liang-Ying Wei

Citations in the corpus

• Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2009, methodology): “Huarng and Yu [24] described a combining methodology using neural networks to forecast fuzzy time series.”
• Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, methodology): “The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base.”
• A HYBRID RECURRENT NEURAL NETWORKS MODEL BASED ON SYNTHESIS FEATURES TO FORECAST THE TAIWAN STOCK MARKET (2012, methodology): “Huarng and Yu [12] applied a backpropagation neural network to establish fuzzy relationships in fuzzy time series for forecasting stock price.”