Time Series Anomaly Detection of International Tourist Arrivals Using Copula-Based Outlier Detection Method
Abstract View: 35,
DOI:
https://doi.org/10.32665/statkom.v5i1.6427Keywords:
Anomaly, Copulas-Based Outlier Detection, International Tourists, LSTMAbstract
Background: An outlier or anomaly is an observation that deviates from normal historical patterns. An observation may appear normal individually, but can be identified as anomalous when evaluated through its dependence on other variables. Copula-based outlier detection (COPOD) accommodates multiple variables using empirical marginal distributions and tail dependence structures to identify anomalies.
Objective: This study aims to detect anomalies in the number of international tourists in Indonesia by considering the variables of inflation and the rupiah exchange rate, as well as to evaluate the handling of anomalies on the forecasting performance of long short-term memory (LSTM).
Methods: Monthly data from January 2000 to December 2025 obtained from CEIC Data, Statistics Indonesia, and Bank Indonesia were used in the study. The analysis includes data exploration and the development of feature engineering, anomaly detection using COPOD, followed by LSTM forecasting.
Results: Detection was carried out based on twelve variables resulting from feature engineering, and eleven periods were identified as anomalies. The forecasting results show better accuracy in the model after handling with a mean absolute percentage error, root mean square error, and Pearson correlation between actual and predicted data, which were 7.494%, 99233, and 0.864, respectively, on the test data.
Conclusion: That good accuracy result can’t be separated from the precision in detecting anomalies. Further research is expected to add relevant variables and develop feature engineering.
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