Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method
Rudra Kalyan Nayak1, S.Y.H. Pavitra2, Ramamani Tripathy3, K. Prathyusha4
1Dr.Rudra Kalyan Nayak*, Assoc. Professor of CSE,KoneruLakshmaiah Education Foundation (Deemed to be University), AP, India.
2S.Y.H. Pavitra, Student of 4 th YearB.Tech (CSE), KoneruLakshmaiah Education Foundation (Deemed to be University), AP, India.
3Dr.Ramamani Tripathy, Asst. Professor of MCA, United School of Business Management,Odisha, India.
4K. Prathyusha, Student of 4 th YearB.Tech (CSE), KoneruLakshmaiah Education Foundation (Deemed to be University), AP, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2858-2863 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3551129219/2019©BEIESP | DOI: 10.35940/ijeat.B3551.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the growing population in the world, economic stability varies day by day. In case of India all banking transaction rules and regulations are taken by Reserve bank of India (RBI) whereas for other countries it is different. Therefore numerous academicians have projected their research on forecasting the currency exchange rate for diverse countryside. Foreign currency exchange rate prediction is a very pivotal task for international market. Hence researchers have explored different methods for predicting foreign currency exchange rate. In this work, we have taken Indian rupees (INR) with two different country’s data set such as Japanese yen (JPY) andChinese Yuan (CNY)for daily, weekly and monthlyprediction beforehand. We implemented a hybrid model oflong short-term memory (LSTM) with K-nearest neighbour (KNN) which gives better opening price prediction accuracy on our dataset. The accuracy of the prediction results are measured by the help of performance standards such as mean absolute percentage error (MAPE) and root mean square error (RMSE).
Keywords: Currency exchange rate, LSTM, KNN, RBI.