Comparative analysis of naïve bayes and knn on prediction of forex price movements for gbp/usd currency at time frame daily

Abstract

Abstract This study aims to analyze the comparison of the Naïve Bayes and kNN on the Prediction of Forex Price Movements for GBP / USD on Time Frame Daily. The data used is taken from the metatrader-4 application which is often used by forex traders when making transactions. There are 2,145 data rows consisting of the date, hour, open price, high, low, close, and transaction volume columns. From this data, a column for the target class is created with the name ‘result’. The result column is filled with increasing or decreasing values. The value of increase or decrease is obtained from the comparison of the previous closing price with the closing price of the next day. This study analyzes the results of the comparison of the data mining classification of algorithm between the Naïve Bayes algorithm and kNN. The 2,145 data were divided into 2 parts, namely 80% for training data and 20% for testing data. The analysis is done by comparing the precision, recall, and accuracy test results for each algorithm. The conclusion of this study is that the kNN algorithm is better than the Naïve Bayes algorithm in case of predicting forex price movements for GBP/USD currency at time frame daily.