Simulation Study to Identify Factors Affecting the Performance of LSTM and XGBoost for Anomaly Detection on Labeled Time Series Data

Simulation Study to Identify Factors Affecting the Performance of LSTM and XGBoost for Anomaly Detection on Labeled Time Series Data

Time series analysis has evolved to include forecasting and anomaly detection, which can be applied in various fields. Machine learning methods, such as long short-term memory (LSTM) and extreme gradient boosting (XGBoost), are widely developed because they are considered superior to conventional me...

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Bibliographic Details
Main Authors: Muhammad Rizky Nurhambali, Yenni Angraini, Anwar Fitrianto
Format: Article
Language:Indonesian
Published: Universitas Muhammadiyah Purwokerto 2025-08-01
Series:Jurnal Informatika
Subjects:
anomaly detection
forecasting
lstm
time series
xgboost
Online Access:http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/26604
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