By providing a range of values rather than a point estimate, accurate interval forecasting is essential to the success of investment decisions in exchange rate markets. This study develops a sliding-window metaheuristic optimization for interval-valued time series forecasting using multi-output least squares support vector regression (MLSSVR). The hyperparameters in MLSSVR are optimized using an accelerated particle swarm optimization algorithm to generate the best predictions and the fastest convergence. The proposed system has a graphical user interface developed in a computing environment and functions as a stand-alone application. The system is validated with stock price as well as exchange rates and outcomes are compared with previous results. Finally, the proposed interval time series prediction approach is tested in two case studies, one is the daily Australian dollar and Japanese yen rates (AUD/JPY) and the other involves US dollar and Canadian dollar rates (USD/CAD). The proposed model is promising for interval time series forecasting.

By providing a range of values rather than a point estimate, accurate interval forecasting is essential to the success of investment decisions in exchange rate markets. This study develops a sliding-window metaheuristic optimization for interval-valued time series forecasting using multi-output least squares support vector regression (MLSSVR). The hyperparameters in MLSSVR are optimized using an accelerated particle swarm optimization algorithm to generate the best predictions and the fastest convergence. The proposed system has a graphical user interface developed in a computing environment and functions as a stand-alone application. The system is validated with stock price as well as exchange rates and outcomes are compared with previous results. Finally, the proposed interval time series prediction approach is tested in two case studies, one is the daily Australian dollar and Japanese yen rates (AUD/JPY) and the other involves US dollar and Canadian dollar rates (USD/CAD). The proposed model is promising for interval time series forecasting.

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