Araveeporn, Autcha
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Item type:Publication, Comparative Evaluation of Event-Based Forecasting Models for Thai Airport Passenger Traffic(2026-02-01); Accurate passenger traffic forecasting is vital for strategic planning in Thailand’s aviation industry. This study forecasts the monthly total number of passengers at Suvarnabhumi (BKK), Don Mueang (DMK), Chiang Mai (CNX), and Phuket (HKT) airports using data from 2017 to 2024. The dataset was partitioned into training (January 2017–December 2023) and testing (January–December 2024) sets. Six methods were compared: Single Exponential Smoothing, Holt’s, Holt’s with Events Adjustment, Holt–Winters Multiplicative, TBATS model, and Box–Jenkins. Performance was evaluated using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results indicate that the optimal forecasting method varies by airport characteristics. Holt’s Method with Events Adjustment, which incorporates major disruptions such as the COVID-19 pandemic, produced the most accurate forecasts for BKK and DMK by effectively capturing external shocks. In contrast, the Holt–Winters Multiplicative method performed best for CNX and HKT, reflecting strong seasonal patterns typically driven by tourism activities in these destinations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Use of Spline Techniques in the Nonparametric Regression Analysis for the Sequence Data with a Random Walk Process(2025-01-01); This study evaluates and compares various spline techniques in the nonparametric regression analysis, specifically focusing on the smoothing spline regression, the natural spline regression, the B-spline regression, and the penalized spline regression. The dependent variable in this analysis is time series data generated by a random walk process, while the independent variable is represented as sequential data. The simulation data, derived from a random walk process with diverse variances and sample sizes, ensures an absence of fixed patterns in the variable's changes. In addition, real-world data from the monthly trading volume of the SET (Stock Exchange of Thailand) index is used for practical application. The criterion for model efficiency estimation is based on minimizing the average mean square error for the simulation and SET index data. At the same time, predictive performance for future values is assessed through the minimum of average mean absolute percentage error. Among the models tested, the natural spline regression achieved the minimum average mean square error in all simulations due to SET index data estimation, excelling in model fit. However, the B-spline regression proved highly effective for forecasting future values.