Name
Title | ||
|---|---|---|
Sequential combination of statistics, econometrics and Adaptive Neural-Fuzzy Interface for stock market prediction |
Abstract | ||
|---|---|---|
Since last decade advanced data simulations help to identify hidden trends in a time series. Our purpose is to identify uncertainties during recession period using statistical analysis, econometrical analysis and Adaptive Neural-Fuzzy networks. In this paper, initially through computational analysis we are testing financial data using correlation tests, likelihood tests, heteroscedastic characteristics analysis and hypothesis tests. These statistical and econometrical tests give us exact nature of data set and relation between data points. All tests and analysis are studied on NASDAQ Stock Market over last 2-years. Then after, optimized subtractive data clustering method is used to cluster the data and create fuzzy membership functions by using Sugeno-type Fuzzy Interface System (FIS). Finally, we are using optimized hybrid learning algorithm in customized Adaptive Neural Fuzzy Interface System (ANFIS) to train the network. Hence, we got an efficient Adaptive Neural-Fuzzy network to check and test the data sets and use it for forecasting the stock market index. During this, the hybrid learning algorithm combines Least-Square method and the Back-propagation gradient descent methods for training the Fuzzy Interface System (FIS) with the help of optimized membership functions and parameters. This paper presents a state-of-art for Adaptive Neural-Fuzzy Network (ANFN) application to forecast stock market index and involved market uncertainties by combining the econometrical test to optimize the ANFIS and FIS function. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2009.12.083 | Expert Syst. Appl. |
Keywords | Field | DocType |
hybrid learning algorithm,q ),computational analysis,adaptive neural-fuzzy interface,sugeno-type fis,data point,data simulation,econometrical analysis,adaptive neural-fuzzy network,garch/gjr/egarch ( p,stock market prediction,econometrical test,stock market index,sequential combination,financial data,optimized subtractive data,adaptive neuro-fuzzy interface system,subtractive clustering,q,back propagation,hypothesis test,statistical analysis,membership function,neuro fuzzy,gradient descent method,indexation,time series,data clustering,least square method | Econometrics,Data mining,Computer science,Artificial intelligence,Adaptive neuro fuzzy inference system,Cluster analysis,Stock market prediction,Stock market,Statistical hypothesis testing,Data point,Stock market index,Fuzzy logic,Statistics,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 7 | Expert Systems With Applications |
Citations | PageRank | References |
5 | 0.50 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tanvir Ansari | 1 | 5 | 0.50 |
| Manoj Kumar | 2 | 732 | 104.98 |
| Anupam Shukla | 3 | 158 | 22.92 |
| Joydip Dhar | 4 | 37 | 12.11 |
| Ritu Tiwari | 5 | 222 | 29.33 |