For time series forecasting, different artificial neural network (ANN) and hybrid models are recommended as alternatives to commonly used autoregressive integrated moving average (ARIMA) models. Recently, combined models with both linear and nonlinear models have greater attention. In this article, ARIMA, linear ANN, multilayer perceptron (MLP), and radial basis function network (RBFN) models are considered along with various combinations of these models for forecasting tourist arrivals to Turkey. Comparison of forecasting performances shows that models with nonlinear components give a better performance.
It is known from the scientific researches that artificial neural networks are alternatives of statistical methods such as regression analysis and classification in recent years. Since multi-layer backpropagation neural network models are nonlinear, it is expected that the neural network models should make better classifications and predictions. The studies on this subject support that idea. In this study, a macro-economic problem on rescheduling or non-rescheduling of the countries' international debts is taken into account. Among the statistical methods, logistic and probit regression, and the different neural network backpropagation algorithms are applied and comparisons are made. Evaluations and suggestions are made depending on the results and different neural network architecture.
Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.
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