Paper Info
Title
Using Interactive Artificial Bee Colony to Forecast Exchange Rate
Abstract
Exchange rate forecasting has become a popular research topic in recent years because the problems of the forecasting model selection and the improvement on forecasting accuracy are not easy to be solved. In this study, we employ a swarm intelligence method called Interactive Artificial Bee Colony (IABC) and use nine macroeconomic factors as the input for the exchange rate forecasting. The sliding window is used in the experiment for both the training and the testing. In our experiments, we use continuous previous three days data as the training set, and use the training result to forecast the fourth day's exchange rage. Moreover, we evaluate the forecasting accuracy with three criteria, namely, Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The experimental results indicate that using IABC with the macroeconomic factors is a positive and doable way for the exchange rate forecasting.
Year
DOI
Venue
2013
10.1109/RVSP.2013.37
RVSP
Keywords
Field
DocType
forecasting accuracy,macroeconomic factor,training set,mean absolute error,forecasting model selection,forecast exchange rate,root mean square error,interactive artificial bee colony,training result,mean square error,exchange rage,exchange rate forecasting,accuracy,sociology,predictive models,optimization,forecasting,particle swarm optimization
Training set,Particle swarm optimization,Sliding window protocol,Swarm intelligence,Model selection,Mean absolute error,Mean squared error,Artificial intelligence,Statistics,Exchange rate
Conference
ISSN
Citations 
PageRank 
2376-9793
0
0.34
References 
Authors
7
3
Name
Order
Citations
PageRank
Jui-Fang Chang111313.78
Chun-Tsung Hsiao200.68
Tsai Pei-wei312715.88