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

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:English
English
Published: Springer Singapore 2024
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/41147/
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

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