Accuracy of forecasting models using Bootstrap test and Friedman's test.

In Time Series analysis, theoretical and empirical findings have suggested that integrating various types of forecasting models can be an effective way to improve the predicting performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decay a period arrangement into its linear and nonlinear parts are one of the main sorts of the Hybrid models for the time series predicting. An attempt is made in this paper to forecast the daily prices of silver, gold metals and foreign exchange rates of Indian rupee (INR) against US dollar (USD) using conventional time series models, artificial neural networks (ANN) and Hybrid models to examine the forecasting capability of Hybrid, Neural Networks and Box-Jenkins models using Bootstrap test and Friedman's test. At a glance from the study, the Hybrid model has more accuracy in forecasting the forecasts of various data sets than that of Box-Jenkins and FFNN model. [ABSTRACT FROM AUTHOR]