Stock Prediction Using Functional Link Artificial Neural Network (FLANN)
| Autor: | Abhinandan Gupta, Dev Kumar Chaudhary, Tanupriya Choudhury |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
Computer science 05 social sciences Feed forward 02 engineering and technology Stock prediction computer.software_genre Perceptron Stock price Stock exchange Technical analysis 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining 050207 economics computer Stock (geology) |
| Zdroj: | 2017 3rd International Conference on Computational Intelligence and Networks (CINE). |
| DOI: | 10.1109/cine.2017.25 |
| Popis: | Stock exchange that is, buying and selling of stock is considered to be an important factor in the economy sector. The Stockbrokers typically use time series or technical analysis in predicting the stock price. These techniques are based on trends and not the actual stock value. Therefore a method of prediction which takes into account the historical values of stock is desired. Neural Networks once again have become famous for prediction of stock. This is due to their ability to deal with non-linear data. The use of Artificial Neural Networks to for predicting the stock prices is proposed in this paper. The input features to the model sometimes can be non-related to the output. Hence, Functional Link Artificial Neural Networks is used here to increase the number of related features in the form of inputs. The data is taken from NSE and is converted into a suitable form for FLANN and then prediction is carried out using Multi-layer feed forward Perceptron model. |
| Databáze: | OpenAIRE |
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