Abstract
Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data.
Highlights
The increases use of data in digital form and databases in a wide range in different fields led to the large quantity of these data, it was necessary to develop tools and algorithms to help extract information from these data and find useful information
One method widely used in data mining is Decision trees which is one of the methods used for the purpose of classification and finding a regression to predict the value of a variable object In addition a more accurate prediction method was proposed [2, 3], in this paper introduced view of boosting algorithm as iterative functional gradient descent algorithms
Implementation and Result Proposed System was based on data published by the Central Bank of Iraq for the daily auction of dollars for the years (2015–2016–2017), respectively, which amounted to 479 auction sessions after the deletion of the holidays where there is no auction
Summary
The increases use of data in digital form and databases in a wide range in different fields led to the large quantity of these data, it was necessary to develop tools and algorithms to help extract information from these data and find useful information. New field in artificial intelligence, called data mining, it has emerged as a technique aimed for extracting knowledge [1] This technique has become more popular in the information age through the exploration of large quantities of data using the techniques of (Pattern Recognition, integration of mathematical methods and statistical information technology) led to possibilities to predict future behavior that helps in decision-making. Implementation and Result Proposed System was based on data published by the Central Bank of Iraq for the daily auction of dollars for the years (2015–2016–2017), respectively, which amounted to 479 auction sessions after the deletion of the holidays where there is no auction As these indicators reflect the comprehensive views of currency exchange rates at the official auction price, market price and quantity offered.
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