Data Imputation Techniques Using the Bag of Functions: Addressing Variable Input Lengths and Missing Data in Time Series Decomposition

In time series analysis, the ability to effectively handle data with varying input lengths and missing data is crucial for accurate modeling. This paper presents the Bag-of-Functions-Driven Imputation framework, which leverages sequence-length independent techniques to decompose time series data whi...

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Veröffentlicht in:	IEEE International Conference on Industrial Technology (Online) S. 1 - 7
Hauptverfasser:	Salazar Torres, David Orlando, Altinses, Diyar, Schwung, Andreas
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 26.03.2025
Schlagworte:	Accuracy Adaptation models Bag of Functions Data models Encoding Finance Imputation Learning systems Medical services Monitoring Synthetic temporal datasets Time series analysis Time series decomposition Time-Invariant Methods
ISSN:	2643-2978
Online-Zugang:	Volltext
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