Efficiency Comparison of Prediction Methods and Analysis of Factors Affecting Savings of People in the Central Region of Thailand

Inarguably, saving is very important for the life of a senior citizen. Artificial neural network (ANN) and multiple linear regression (MLR) analyses have been successfully used to predict and analyze factors affecting the savings of people in several regions of the world. Many studies concluded that ANN is more efficient than MLR. However, some studies concluded that MLR is more efficient. To investigate this issue further, this study directly compared the efficiencies of unoptimized ANN and MLR in predicting and analyzing factors affecting the savings of people in the central region of Thailand in 2019, based on secondary data from a household socioeconomic survey, i.e., the National Statistical Staff Household Income Survey. The data were collected from January 2019 to December 2019 from questionnaires distributed to samples of households. The savings of people in the 25 provinces of Thailand were investigated with MLR and unoptimized ANN. Their prediction efficiencies were compared in terms of root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and processing time. The results showed that for all categories of savings—savings of low-, middle-, and high-income households—MLR was faster in processing time. It also provided a lower RMSE and a higher R2 than the unoptimized ANN. Nevertheless, unoptimized ANN provided a lower MAE than MLR for the savings of low- and high-income household data. The most important factor affecting the savings of low-, middle-, and high-income households was the factor of deposit interest, bond, share dividends, and other types of investment.