Interval Forecasting of Financial Time Series by Accelerated Particle Swarm-Optimized Multi-Output Machine Learning System

By providing a range of values rather than a point estimate, accurate interval forecasting is critical to the success of investment decisions in exchange rate markets. This work proposes 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 finetuned using an accelerated particle swarm optimization algorithm to yield the best predictions and fastest convergence. The proposed system has a graphical user interface that is developed in a computing environment and functions as a stand-alone application. The system is validated using stock prices as well as exchange rates and outputs are compared with published results. Finally, the proposed interval time series prediction method is tested in two case studies; one involves 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.