Forex Analysis on USD to INR Conversion: A Comparative Analysis of Multiple Statistical and Machine Learning Algorithms
- Title
- Forex Analysis on USD to INR Conversion: A Comparative Analysis of Multiple Statistical and Machine Learning Algorithms
- Creator
- Fatima A.; Sarangi P.K.; Sahoo A.K.; Dutta M.; Nayak S.R.; Sinha D.
- Description
- Foreign Currency Exchange (FOREX) engages a major role in world economy and the international market. It is a vast study based on determining whether or not to wait, buy or sell on a trading currency pair. The main objective is to predict the future currency prices using historical data in order to make more informed and accurate investment decisions for business traders and monetary market. This work experimented and implements ten machine learning strategies namely Random Forest, Decision Tree, Support vector regressor (SVM), Linear SVM, Linear Regression, Ridge, Lasso, K-Nearest Neighbor (KNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to assess the historical data and help the traders to invest in foreign currency exchange. The dataset used to validate and verify the machine learning algorithms is available in public domain and it is the daily Foreign Currency Exchange price of United States Dollars (USD) to Indian Rupees (INR). The experimented result shows that the Long Short-Term Memory (LSTM) model performs a bit better than the other machine learning models for this particular case. This work straight away does not reject the other methods it rather needs more experimental analysis with other models that has changed architecture and different dataset. 2024 IEEE.
- Source
- Proceedings of the 14th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2024, pp. 870-875.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Decision Tree; FOREX; KNN; Lasso; Linear Regression; Linear SVR; LSTM; Random Forest; Ridge; RNN; SVR
- Coverage
- Fatima A., Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India; Sarangi P.K., Chitkara University School of Engineering & Technology, Chitkara University, Himachal Pradesh, India; Sahoo A.K., Graphic Era Hill University, Dehradun, India; Dutta M., Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India; Nayak S.R., School of Computer Engineering, Kiit Deemed to Be University, Odisha, Bhubaneswar, 751024, India; Sinha D., Christ (Deemed to Be University), Delhi NCR, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835034483-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Fatima A.; Sarangi P.K.; Sahoo A.K.; Dutta M.; Nayak S.R.; Sinha D., “Forex Analysis on USD to INR Conversion: A Comparative Analysis of Multiple Statistical and Machine Learning Algorithms,” CHRIST (Deemed To Be University) Institutional Repository, accessed March 3, 2025, https://archives.christuniversity.in/items/show/19525.