Name
Abstract | ||
|---|---|---|
A wide range of applications can be identified for time series prediction, including energy systems planning, currency forecasting, or traffic prediction. Specifically, stock exchange operations can greatly benefit from efficient forecast techniques. Therefore, a number of different prediction approaches have been proposed such as linear models, Feedforward Neural network models, Recurrent Neural networks or Fuzzy Neural Models. In this paper one presents a prediction model based on fuzzy rules that relate past data values with the next unknown value to be estimated. A Fuzzy Boolean Neural Network has been used for this purpose, which has been applied to the Nasdaq index prediction. The results turned to be encouraging, namely on the percentage of correct up/down trend prediction. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICHIS.2005.71 | HIS |
Keywords | Field | DocType |
nasdaq index prediction,fuzzy boolean neural network,trend prediction,traffic prediction,recurrent neural network,feedforward neural network model,fuzzy neural models,different prediction approach,market index prediction,fuzzy boolean nets,time series prediction,prediction model,fuzzy set theory,stock exchange,feedforward neural network,neural nets,indexation,boolean algebra,economic indicators,linear model,neural network | Data mining,Time series,Neuro-fuzzy,Feedforward neural network,Computer science,Fuzzy logic,Recurrent neural network,Fuzzy set,Artificial intelligence,Artificial neural network,Fuzzy number,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2457-5 | 2 | 0.48 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose A. B. Tome | 1 | 6 | 1.01 |
| Joao Paulo Carvalho | 2 | 56 | 5.01 |