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
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Zusammenfassung: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 while accommodating inputs of differing sizes. Unlike traditional methods that require uniform input lengths, the Padding-BoF framework employs a flexible encoding approach, allowing for the integration of variable-length time series and missing elements in the data. Through a series of experiments, we demonstrate that the BoF framework not only ensures precise reconstruction of the original data but also enhances data imputation capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling and disparate operational cycles, making it a valuable tool for applications in fields such as finance, healthcare, and industrial monitoring.
ISSN:2643-2978
DOI:10.1109/ICIT63637.2025.10965229