USP Electronic Research Repository

Forecasting of Currency Exchange Rate Using Artificial Neural Network: A Case Study of Solomon Island Dollar

Kimata, James D and Khan, Mohammad G.M. and Sharma, Anuraganand and Rashid, Mahmood and Tekabu, Tokaua (2019) Forecasting of Currency Exchange Rate Using Artificial Neural Network: A Case Study of Solomon Island Dollar. In: PRICAI 2019: Trends in Artificial Intelligence. Springer Nature, Switzerland, pp. 729-733. ISBN 978-3-030-29893-7

[img] PDF - Accepted Version
Restricted to Repository staff only

Download (1620Kb)

    Abstract

    The use of neural network models for currency exchange rate forecasting has received much attention in recent time. In this paper, we propose an exchange rate forecasting model based on artificial neural network. We tested our model on forecasting the exchange rate of Solomon Islands Dollar against some major trading currencies of the country such as, Australian Dollar, Great Britain Pound, Japanese yen, and Euro. We compared the performance of our model with that of the single exponential smoothing model; the double exponential smoothing with trend model; and Holt-Winter multiplicative and additive seasonal and multiple linear regression model. The performance of the models was measured using the error function, root mean square error (RMSE). The empirical result reveals that the proposed model is more efficient and accurate in forecasting currency exchange rate in comparison to the regression and time series models.

    Item Type: Book Chapter
    Additional Information: DOI: https://doi.org/10.1007/978-3-030-29894-4_58
    Subjects: Q Science > QA Mathematics
    Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Fulori Nainoca - Waqairagata
    Date Deposited: 11 Feb 2020 11:48
    Last Modified: 24 Jun 2020 16:39
    URI: http://repository.usp.ac.fj/id/eprint/11961
    UNSPECIFIED

    Actions (login required)

    View Item

    Document Downloads

    More statistics for this item...