TLBO based hybrid forecasting model for prediction of exchange rates

Abstract

The teacher-learning based optimization (TLBO) algorithm is a new meta-heuristic approach, having the ability to solve non-linear problem and free from algorithm parameters. This paper proposes an efficient prediction model for forecasting currency exchange rate in term of 1 US Dollar to Indian Rupees, Singapore Dollar and Canadian Dollar using FLANN (Functional Link Artificial Neural Network). The teaching and learning algorithm has been used to optimize the weights of the forecasting models. The mean absolute percentage error (MAPE) is used to find out the performance of the model. The performance of the model is evaluated through simulation study and the results have been compared with FLANN-PSO and FLANN-DE forecasting models. It is observed that the model gives better performance result.