Performance comparison of artificial neural network techniques for foreign exchange rate forecasting
- Title
- Performance comparison of artificial neural network techniques for foreign exchange rate forecasting
- Creator
- Kumar Chandar S.; Sumathi M.; Sivanandam S.N.
- Description
- Artificial Neural Networks is one of the promising techniques for forecasting financial time series markets and business. In this paper, Radial Basis Function is used to forecast the daily foreign exchange rate of USD in terms of Indian rupees in India during the period 2009-2014. Here, seven technical indicators like simple moving average of one week, Two week, Momentum, Price rate of change, Disparity 7, Disparity 14, Price oscillator are proposed as inputs for forecasting the time series. In addition, this study compares the four models namely Pattern Recognition Networks, Feed Forward Back Propagation Networks, Feed Forward Networks with no feedback, and Radial Basis Function Network to forecast the daily currency exchange rate during the period. The performance of all these models are analysed from accuracy measures namely Mean Square Error, Mean Absolute Error, Sum Square Error and Root Mean Square Error. From the simulation results, the average performance of Radial Basis Function network was found considerably better than the other networks. Research India Publications.
- Source
- International Journal of Applied Engineering Research, Vol-9, No. 24, pp. 27433-27455.
- Date
- 2014-01-01
- Publisher
- Research India Publications
- Subject
- Feed forward back propagation networks; Feed forward networks; Financial time series; Pattern recognition networks radial basis function; Technical indicators
- Coverage
- Kumar Chandar S., Madurai Kamaraj University, Madurai/CHRIST University, Bangalore, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, India
- Rights
- Restricted Access
- Relation
- ISSN: 9734562
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Kumar Chandar S.; Sumathi M.; Sivanandam S.N., “Performance comparison of artificial neural network techniques for foreign exchange rate forecasting,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/17280.