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Floating exchange rate system and several factors made it hard to forecast changes in exchange rate on a daily basis. However, taking several factors into account can predict changes in exchange rate. Therefore, this study aims to forecast daily changes in exchange rate between Thai Baht and US Dollar by using data mining technique. 9 algorithms were used to forecast: 1) Naive Bayes 2) Generalized Linear Model 3) Logistic Regression 4) Fast Large Margin 5) Deep Learning 6) Decision Tree 7) Random Forest 8) Gradient Boosted Trees, and 9) Support Vector Machine. Each algorithm was tested accuracy by using 10-fold cross validation with train/test ratio following: 90:10, 80:20 to 10:90 respectively.17 factors were used to analyze data for example, exchange rate between Thai Baht to US Dollar, gold prices, US Dollar price index, crude oil price, price stock exchange index in Thailand, USA, Europe, Britain, Japan, and China. Dataset from January 3, 2002 to April 18, 2019 were used to categorize data. The data were collected from the Bank of Thailand, the Federal Reserve Bank of Saint Louis, th.investing.com, and finance.yahoo. The results reported that Logistic Regression was reached the highest accuracy at 64.86% in train/test portion 80:20, Fast Large Margin was reported at 64.66% in train/test portion 80:20 and 90:10, whereas Logistic Regression was exhibited at 64.61% in train/test portion of 70:30. Decision Tree was shown the lowest accuracy at 57.74% in train/test portion of 20:80. Three factors: US Dollar price index, gold price, and Nasdaq price index were respectively reported as the three most significant correlation of changes in exchange rate. The least factor was Nikkei price index. The result shows that the proposed techniques can be used to support exchange risk management and to forecast other foreign exchange rates.
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 Foreign Exchange Risk Management, https://www.bot.or.th/English/FinancialMarkets/Foreign ExchangeMarket/ForeignExchangeHedgingManual/Pages/default.aspx, 21 June 2019.
 A.R. Nagpure, Prediction of Multi-Currency Exchange Rates Using Deep Learning, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6) (2019), 2278-3075.
 A.S. Babu, S.K. Reddy, Exchange Rate Forecasting using ARIMA, Neural Network and Fuzzy Neuron, Journal of Stock Forex Trading, 4(3) (2015), 1-5.
 D. Nayab, G.M. Khan, S.A. Mahmud, Prediction of Foreign Currency Exchange Rates Using CGPANN, International Conference on Engineering Applications of Neural Networks, Halkidiki, Greece, 13-16 September 2013, 91-101.
 S. Galeshchuk, S. Mukherjee, Deep Networks for Predicting Direction of Change in Foreign Exchange Rates, Intelligent Systems in Accounting, Finance and Management, 24(4) (2017), 100-110.
 D.K. Shamar, S.H. Hota, R. Handa, Prediction of Foreign Exchange Rate using Regression Techniques, Review of Business and Technology Research, 14(1) (2017), 29-33.
 J.Z. Ling, A.K. Tsui, Z.Y. Zhang, Forecast Foreign Exchange with both Linear and Nonlinear Models Coupled with Trading Rules for Selected Currencies, The 21st International Congress on Modelling and Simulation (MODSIM2015), Gold Coast Convention and Exhibition Centre, Broadbeach, Queensland, Australia, 29 November - 4 December 2015, 1112-1118.
 A. Jansod, Accuracy Comparison in Foreign Exchange Rate Forecasting Between Neural Networks and ARIMA GARCH-M Models, M.Econ. Economics, Chiang Mai University, Chiang Mai, 2007.
 S. Lekkla, J. Thongkam, Forecasting the Trend of Foreign Exchange Rates Using Time Series Analysis Techniques, Journal of Information Technology Management and Innovation. 5(2) 2018, 94-103.
 E. Pacharawongsakda, An Introduction to Data Mining Techniques, first ed., Asia Digital Press Co., Ltd., Bangkok, 2014.
 Rates of Exchange of Commercial Banks in Bangkok Metropolis, https://www.bot.or.th/App/ BTWS_STAT/statistics/BOTWEBSTAT.aspx?reportID=123&language=ENG, 21 April 2019.
 Oil-Economic Data Series, https://fred. stlouisfed.org/tags/series?t=oil, 22 April 2019.
 Indices, https:// th.investing. com/indices/, 22 April 2019.
 Yahoo Finance, https:// finance.yahoo.com/, 22 April 2019.