Adjusting Anomalies in International Tourist Arrivals to North Sumatra During the Peak COVID-19 Period (April 2020 to June 2022) to Enhance the Validity of Time Series Modeling

Eddy, Thaswin
Open Darnius

Journal of Research in Mathematics Trends and Technology
Vol. 7 No. 2 (2025): Journal of Research in Mathematics Trends and Technology (JoRMTT)

The feasibility of time series modeling is significantly influenced by both the availability and the structural patterns of the data. Regular and continuous data collection over time is essential for constructing reliable time series models, particularly for forecasting purposes. Generally, a minimum of 50 time series data points is considered ideal to ensure the robustness and predictive power of such models. However, the presence of extreme fluctuations—such as sharp spikes or drops—can severely affect the integrity of the model. In the context of international tourist arrivals to North Sumatra during the peak period of the COVID-19 pandemic (April 2020 to June 2022), substantial data anomalies were observed. The results of modifying these anomalies indicate that increasing the number of adjusted data points during this period leads to a greater number of feasible time series models suitable for predictive analysis.