Imputation Analysis of Time-Series Data Using a Random Forest Algorithm

• Main Authors: Nur Najmiyah, Jaafar, Muhammad Nur Ajmal, Rosdi, Khairur Rijal, Jamaludin, Faizir, Ramlie, Habibah, Abdul Talib
• Format: Conference or Workshop Item
• Language: English; English
• Published: Springer Singapore 2024
• Subjects: TS Manufactures
• Online Access: http://umpir.ump.edu.my/id/eprint/41147/1/Imputation%20Analysis%20of%20Time-Series%20Data.pdf; http://umpir.ump.edu.my/id/eprint/41147/2/Imputation%20Analysis%20of%20Time-Series%20Data%20Using%20a%20Random%20Forest%20Algorithm.pdf; http://umpir.ump.edu.my/id/eprint/41147/; https://doi.org/10.1007/978-981-99-8819-8_4

Missing data poses a significant challenge in extensive datasets, particularly those containing time-series information, leading to potential inaccuracies in data analysis and machine learning model development. To address the issue, this paper compared and evaluated four imputation methods: MissFor...