CNN-RNN based Hybrid Machine Learning Model to Predict the Currency Exchange Rate: USD to INR
- Title
- CNN-RNN based Hybrid Machine Learning Model to Predict the Currency Exchange Rate: USD to INR
- Creator
- Singh G.; Sarangi P.K.; Rani L.; Sharma K.; Sinha S.; Sahoo A.K.; Rath B.P.
- Description
- Foreign currency exchange plays an imperative part in the global business and in monetary market. It is also an opportunity for many traders as an investment option and the advance knowledge of fluctuation helps the investors making right decision on time. However, due to its volatile nature, prediction of foreign currency exchange is a challenging task. This paper implements two models based on machine learning, namely Recurrent Neural Networks (RNN) and a Hybrid model of Convolutional Neural Networks (CNN) with RNN known as CNN-RNN to assess the accuracy in predicting the conversion rate of US Dollar (USD) to Indian Rupees (INR). The data set used to verify and validate the models is the daily currency exchange rate (USD to INR) available in public domain. The experimental results show that the simple RNN model performs slightly better than the hybrid model in this particular case. Though the accuracy of the hybrid model is very high in terms of error calculation still the single RNN model is the better performer. This does not straight away reject the hybrid model rather needs more experimental analysis with changing architecture and data set. 2022 IEEE.
- Source
- 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 1668-1672.
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- CNN; DNN; FLANN; LSTM; RNN
- Coverage
- Singh G., Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Department of Computer Science and Engineering, India; Sarangi P.K., Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Department of Computer Science and Engineering, India; Rani L., Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Department of Computer Science and Engineering, India; Sharma K., Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Department of Computer Science and Engineering, India; Sinha S., Christ University, Department of Computer Science and Engineering, Bangalore, India; Sahoo A.K., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Rath B.P., Cognizant Technology Solutions Ltd., Department of Computer Science and Engineering, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166543789-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Singh G.; Sarangi P.K.; Rani L.; Sharma K.; Sinha S.; Sahoo A.K.; Rath B.P., “CNN-RNN based Hybrid Machine Learning Model to Predict the Currency Exchange Rate: USD to INR,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/20270.