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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| Published in: | IEEE International Conference on Industrial Technology (Online) pp. 1 - 7 |
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| Format: | Conference Proceeding |
| Language: | English |
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IEEE
26.03.2025
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| ISSN: | 2643-2978 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Salazar Torres, David Orlando Altinses, Diyar Schwung, Andreas |
| Author_xml | – sequence: 1 givenname: David Orlando surname: Salazar Torres fullname: Salazar Torres, David Orlando email: salazartorres.davidorlando@fh-swf.de organization: South Westphalia University of Applied Sciences,Department of Automation Technology and learning systems,Soest,Germany – sequence: 2 givenname: Diyar surname: Altinses fullname: Altinses, Diyar email: altinses.diyar@fh-swf.de organization: South Westphalia University of Applied Sciences,Department of Automation Technology and learning systems,Soest,Germany – sequence: 3 givenname: Andreas surname: Schwung fullname: Schwung, Andreas email: schwung.andreas@fh-swf.de organization: South Westphalia University of Applied Sciences,Department of Automation Technology and learning systems,Soest,Germany |
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| Snippet | In time series analysis, the ability to effectively handle data with varying input lengths and missing data is crucial for accurate modeling. This paper... |
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| SubjectTerms | 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 |
| Title | Data Imputation Techniques Using the Bag of Functions: Addressing Variable Input Lengths and Missing Data in Time Series Decomposition |
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