Citation:

Andhalia Liza Marie, Regi Muzio Ponziani. "Does Forecasting Using Regression Fare Better Than Other Methods?" International Research Journal of Economics and Management Studies, Vol. 4, No. 5, pp. 374-381, 2025.

Abstract:

This research aims to find the best model to forecast the number of tourists visiting Indonesia among various methods, including multiple regression. The research period was from January 2017 until August 2024. Therefore, the period of the pandemic was covered in this research. The models tested were exponential smoothing, double exponential smoothing, multiple regression, and moving average. The research found that the moving average with the order of 2 is the best-performing model with an error percentage of around 6%. The regression model performs fairly well, with a MAPE of around 16%. The parameters in the regression are all significant, with an adjusted R-squared of 84.49%. This means we can control COVID in the regression model to have a MAPE score below 20%. The second and third best models are exponential smoothing with α of 0.8 and double exponential smoothing with α and β of 0.8. Since the MAPE for exponential and double exponential smoothing does not vary widely, we can infer that the trend component in exponential smoothing does not contribute much to the forecasting accuracy. Future research can include a more established regression, such as ridge regression or lasso regression, for forecasting purposes and compare their performance with machine learning/deep learning models. This will shed light on the efficacy of time series econometrics performance, especially compared to the recent machine learning/deep learning development.

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Keywords:

Double Exponential Smoothing, Exponential Smoothing, Forecasting, Moving Average, Regression.