Open AccessForecasting foreign exchange rate using a combination of linear regression and flower pollination algorithm
Author(s) -
I. B. N. Pascima,
I Made Putrama
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1810/1/012021
Subject(s) - currency , regression , computer science , population , mean squared error , regression analysis , econometrics , hyperparameter , profit (economics) , value (mathematics) , algorithm , statistics , machine learning , mathematics , economics , demography , sociology , monetary economics , microeconomics
Several currencies exist in the world. Each currency will have value. The currency exchange will go through a conversion process to adjust the amount. Each currency value can fluctuate based on the conditions of the currency area. The fluctuating changes in value provide profit opportunities. Maximizing profits can make forecasts so that the right decisions are made. One of the forecasts can use regression. Regression is capable of forecasting based on historical data. The regression in this study will be optimized using the Flower Pollination Algorithm (FPA). The use of the Flower Pollination Algorithm (FPA) aims to obtain appropriate parameters for regression to reduce forecast errors. The data in this study were obtained by utilizing extraction from the Meta trader application. This data will be the basis for the system learning stage and the testing phase. Obtaining a good hyperparameter can make the forecasting system closer to the actual value. Good system accuracy can be a trader’s supporting data in making transactions. Forecasting in this study used the parameter 5 window sizes, 20 population sizes, and 0.7 probability switch. This experiment resulted in MSE 0.0331 and RMSE 0.1756. This forecasting has sufficient results to support a trader’s decision. Further research is needed to improve accuracy and determine the direction of the forecast to improve this research.