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Concisely, the GSLM with coefficients of determination (R2) 99.957% and root mean squared error (RMSE) of 2.121% outperformed the SVM-FFA with R2 99.59%, RMSE 3.27%, ANN with R2 99.56%, RMSE 3.3%, ANFIS with R2 98.9%, RMSE 4.3%, GP with R2 99.89%, RMSE 3.47%, GEP with R2 94.75%, RMSE 4.15% for forecasting weekly time series. In forecasting monthly time series, the GLSM method with R2 99.517% and RMSE 6.91% also outperformed GEP R2 91.95%, RMSE 15.3%, ANFIS R2 92.85%, RMSE 47.55% models. Consequently, GSLM proved that by applying proper
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