Cite Article

Univariate Financial Time Series Prediction using Clonal Selection Algorithm

Choose citation format

BibTeX

@article{IJASEIT10235,
   author = {Ammar Azlan and Yuhanis Yusof and Mohamad Farhan Mohamad Mohsin},
   title = {Univariate Financial Time Series Prediction using Clonal Selection Algorithm},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {1},
   year = {2020},
   pages = {151--156},
   keywords = {artificial immune system; clonal selection algorithm; financial time series; prediction; univariate.},
   abstract = {

The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10235},    doi = {10.18517/ijaseit.10.1.10235} }

EndNote

%A Azlan, Ammar
%A Yusof, Yuhanis
%A Mohamad Mohsin, Mohamad Farhan
%D 2020
%T Univariate Financial Time Series Prediction using Clonal Selection Algorithm
%B 2020
%9 artificial immune system; clonal selection algorithm; financial time series; prediction; univariate.
%! Univariate Financial Time Series Prediction using Clonal Selection Algorithm
%K artificial immune system; clonal selection algorithm; financial time series; prediction; univariate.
%X 

The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10235 %R doi:10.18517/ijaseit.10.1.10235 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 1 %@ 2088-5334

IEEE

Ammar Azlan,Yuhanis Yusof and Mohamad Farhan Mohamad Mohsin,"Univariate Financial Time Series Prediction using Clonal Selection Algorithm," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 1, pp. 151-156, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.1.10235.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Azlan, Ammar
AU  - Yusof, Yuhanis
AU  - Mohamad Mohsin, Mohamad Farhan
PY  - 2020
TI  - Univariate Financial Time Series Prediction using Clonal Selection Algorithm
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 1
Y2  - 2020
SP  - 151
EP  - 156
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - artificial immune system; clonal selection algorithm; financial time series; prediction; univariate.
N2  - 

The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10235 DO - 10.18517/ijaseit.10.1.10235

RefWorks

RT Journal Article
ID 10235
A1 Azlan, Ammar
A1 Yusof, Yuhanis
A1 Mohamad Mohsin, Mohamad Farhan
T1 Univariate Financial Time Series Prediction using Clonal Selection Algorithm
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 1
YR 2020
SP 151
OP 156
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 artificial immune system; clonal selection algorithm; financial time series; prediction; univariate.
AB 

The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10235 DO - 10.18517/ijaseit.10.1.10235