Comparison of hybrid and non-hybrid models in short-term predictions on time series in the R development environment

Abstract

Because many time series usually contain both linear and nonlinear components, a single linear or nonlinear model may be insufficient for modeling and predicting time series. Therefore, estimation results are tried to be improved by using collaborative models in time series short-term prediction processes. In this study, the performances of both stand-alone models and models whose different combinations can be used in a hybrid environment are compared. The mean absolute percentage error (MAPE) metric values obtained from two different categories were evaluated. In addition, the estimation performances of three different approaches such as equi-weighted (EW), variable-weighted (VW) and cross-validation-weighted (CVW) for hybrid operation were also compared.
 The findings on the container throughput forecast of the Airpassengers dataset reveal that the hybrid model's forecasts outperform the non-combined model.

A Comprehensive Time Series Analysis of Annual Fossil-Based Electrical Energy Production Across Key Nations (1985-2022)

“…RMSE measures the average magnitude of the differences between predicted values and actual values, taking the square root of the average of the squared differences. It provides a single numerical value that represents the typical error of the model's predictions, with lower RMSE indicating better predictive performance.RMSE is sensitive to outliers and penalizes larger errors more heavily due to the squaring operation, making it a widely used measure for assessing the overall goodness-of-fit of predictive models(Kim & Kim, 2016;Zeydin Pala, Atıcı, & Yaldız, 2023;Zeydin Pala & Ünlük, 2022). represents the total number of observations, 𝑦 𝑡 represents the actual value, 𝒚 ̂𝒕 represents the predicted value.Mean Absolute Error (MAE), is a metric used for assessing the accuracy of a predictive model, commonly in the context of regression analysis or time series forecasting.…”