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

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...

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Bibliographic Details

Main Authors:	Nur Najmiyah, Jaafar, Muhammad Nur Ajmal, Rosdi, Khairur Rijal, Jamaludin, Faizir, Ramlie, Habibah, Abdul Talib
Format:	Conference or Workshop Item
Language:	en en
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
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