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Data mining involves the use of different data analysis tools to find unknown patterns and relationships in large data sets. These tools could include statistical and mathematical models, and machine learning tools. The trading in a stock market involves understanding of various techniques and relevant methodology. Trend analysis and prediction played a vital role in practical stock trading. The data mining researchers have put in great efforts to generate rules and the emphasis was always to generate an optimal number of rules so that an emerging pattern could be identified. It explores the modeling of data mining techniques along with the technical analysis approach. The broad objectives of the book is to provide a framework of a model on the basis of data mining concepts and chart analysis feature of evaluation of the movement of stock market. These two techniques i.e. data mining and chart analysis are explored to have efficiency, accuracy and reliability to determine the parameters for developing a model. Performance metrics were used to ascertain the accuracy level and an error level. A model was proposed and a comparison with existing systems was carried out.
| Autorius: | Mukesh Kumar, Arvind Kalia, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2017 |
| Knygos puslapių skaičius: | 224 |
| ISBN-10: | 3330039442 |
| ISBN-13: | 9783330039445 |
| Formatas: | 220 x 150 x 14 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Efficient Mining Of Emerging Patterns In Time Series Stock Data: A Prediction Model“
